Advertisement
Level V Evidence| Volume 5, ISSUE 1, e297-e304, February 2023

Shoulder Range of Motion Measurements and Baseball Elbow Injuries: Ambiguity in Scientific Models, Approach, and Execution is Hurting Overhead Athlete Health

Open AccessPublished:December 12, 2022DOI:https://doi.org/10.1016/j.asmr.2022.11.001

      Abstract

      Elbow injuries are a significant and increasing issue in baseball. Elbow injuries account for 16% of all injuries sustained at the professional level and collegiate level. Because of the continued rise in injury rates, loss of performance value, and medical burden, sports medicine clinicians have attempted to research the causes underlying this injury epidemic in an attempt to help mitigate baseball elbow injuries. Shoulder range of motion (ROM) is the most researched clinical metric related to elbow injuries in baseball and has the greatest consensus as a viable prognostic factor specifically for medial elbow injury. Shoulder ROM is easy to measure, can be modified through stretching and manual therapy interventions, and can be easily assessed during preseason screening throughout all baseball levels. Despite a large number of studies and the widespread use of shoulder ROM in injury risk screening, current findings are unclear as to whether there is a true cause-effect relation with baseball elbow injuries. We argue that the conflicting findings revolving around the value of shoulder ROM measurements associated with baseball elbow injuries are the result of 4 gaps in the research approaches implemented to date: ambiguous research questions, mixed study populations, statistical models used, and shoulder ROM methodology. Specifically, there is a mismatch of methods, statistical models, and conclusions such as (1) investigating the association (i.e., correlation) between shoulder ROM measurements and injury and (2) investigating the cause-effect relation of shoulder ROM to baseball injuries. The purpose of this article is to detail the required scientific steps to evaluate whether preseason shoulder ROM is a potential causal factor for pitching elbow injury. We also provide recommendations to allow for future causal inferences to be made between shoulder ROM and elbow injury. This information will ultimately assist in informing clinical models of care and decision making for baseball throwers.
      Elbow injuries are a significant and increasing issue in baseball.
      • Bullock G.S.
      • Uhan J.
      • Harriss E.K.
      • Arden N.K.
      • Filbay S.R.
      The relationship between baseball participation and health: A systematic scoping review.
      • Posner M.
      • Cameron K.L.
      • Wolf J.M.
      • Belmont Jr., P.J.
      • Owens B.D.
      Epidemiology of Major League Baseball injuries.
      • Conte S.
      • Camp C.L.
      • Dines J.S.
      Injury trends in Major League Baseball over 18 seasons: 1998-2015.
      • Camp C.L.
      • Conte S.
      • D'Angelo J.
      • Fealy S.A.
      Epidemiology of ulnar collateral ligament reconstruction in Major and Minor League Baseball pitchers: Comprehensive report of 1429 cases.
      • Boltz A.J.
      • Powell J.R.
      • Robison H.J.
      • Morris S.N.
      • Collins C.L.
      • Chandran A.
      Epidemiology of injuries in National Collegiate Athletic Association men's baseball: 2014–2015 through 2018–2019.
      Elbow injuries account for 16% of all injuries sustained at the professional level and collegiate level.
      • Posner M.
      • Cameron K.L.
      • Wolf J.M.
      • Belmont Jr., P.J.
      • Owens B.D.
      Epidemiology of Major League Baseball injuries.
      These injuries result in a high medical burden, costing Major League Baseball $395 million over a 10-year span, at $1.9 million per player.
      • Meldau J.E.
      • Srivastava K.
      • Okoroha K.R.
      • Ahmad C.S.
      • Moutzouros V.
      • Makhni E.C.
      Cost analysis of Tommy John surgery for Major League Baseball teams.
      The median time to return from ulnar collateral ligament reconstruction is 17 months.
      • Conte S.
      • Camp C.L.
      • Dines J.S.
      Injury trends in Major League Baseball over 18 seasons: 1998-2015.
      Because of the continued rise in injury rates,
      • Posner M.
      • Cameron K.L.
      • Wolf J.M.
      • Belmont Jr., P.J.
      • Owens B.D.
      Epidemiology of Major League Baseball injuries.
      ,
      • Camp C.L.
      • Conte S.
      • D'Angelo J.
      • Fealy S.A.
      Epidemiology of ulnar collateral ligament reconstruction in Major and Minor League Baseball pitchers: Comprehensive report of 1429 cases.
      loss of performance value,
      • Peters S.D.
      • Bullock G.S.
      • Goode A.P.
      • Garrigues G.E.
      • Ruch D.S.
      • Reiman M.P.
      The success of return to sport after ulnar collateral ligament injury in baseball: A systematic review and meta-analysis.
      and medical burden,
      • Meldau J.E.
      • Srivastava K.
      • Okoroha K.R.
      • Ahmad C.S.
      • Moutzouros V.
      • Makhni E.C.
      Cost analysis of Tommy John surgery for Major League Baseball teams.
      sports medicine clinicians have attempted to research the causes

      Lobb NJ, Lu Z, Long E, Chow K, Michener LA. Sonographic morphological and qualitative deficits in the elbow ulnar collateral ligament and ulnohumeral joint in throwing arms of asymptomatic collegiate baseball pitchers. Skeletal Radiol 2023;52:31-37.

      • Pozzi F.
      • Plummer H.A.
      • Shanley E.
      • et al.
      Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
      • Shanley E.
      • Rauh M.J.
      • Michener L.A.
      • Ellenbecker T.S.
      • Garrison J.C.
      • Thigpen C.A.
      Shoulder range of motion measures as risk factors for shoulder and elbow injuries in high school softball and baseball players.
      underlying this injury epidemic
      • Erickson B.J.
      The epidemic of Tommy John surgery: The role of the orthopedic surgeon.
      in an attempt to help mitigate baseball elbow injuries.
      • Shanley E.
      • Kissenberth M.J.
      • Thigpen C.A.
      • et al.
      Preseason shoulder range of motion screening as a predictor of injury among youth and adolescent baseball pitchers.
      ,
      • Sakata J.
      • Nakamura E.
      • Suzuki T.
      • et al.
      Efficacy of a prevention program for medial elbow injuries in youth baseball players.
      Shoulder range of motion (ROM) is the most researched clinical metric related to elbow injuries in baseball
      • Pozzi F.
      • Plummer H.A.
      • Shanley E.
      • et al.
      Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
      ,
      • Bullock G.S.
      • Faherty M.S.
      • Ledbetter L.
      • Thigpen C.A.
      • Sell T.C.
      Shoulder range of motion and baseball arm injuries: A systematic review and meta-analysis.
      and has the greatest consensus as a viable prognostic factor specifically for medial elbow injury.
      • Wilk K.E.
      • Macrina L.C.
      • Fleisig G.S.
      • et al.
      Deficits in glenohumeral passive range of motion increase risk of shoulder injury in professional baseball pitchers: A prospective study.
      ,
      • Wilk K.E.
      • Macrina L.C.
      • Fleisig G.S.
      • et al.
      Deficits in glenohumeral passive range of motion increase risk of elbow injury in professional baseball pitchers: A prospective study.
      Shoulder ROM is easy to measure,
      • Shanley E.
      • Kissenberth M.J.
      • Thigpen C.A.
      • et al.
      Preseason shoulder range of motion screening as a predictor of injury among youth and adolescent baseball pitchers.
      can be modified through stretching and manual therapy interventions,
      • Bailey L.B.
      • Shanley E.
      • Hawkins R.
      • et al.
      Mechanisms of shoulder range of motion deficits in asymptomatic baseball players.
      ,
      • Bailey L.B.
      • Thigpen C.A.
      • Hawkins R.J.
      • Beattie P.F.
      • Shanley E.
      Effectiveness of manual therapy and stretching for baseball players with shoulder range of motion deficits.
      and can be easily assessed during preseason screening throughout all baseball levels.
      • Pozzi F.
      • Plummer H.A.
      • Shanley E.
      • et al.
      Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
      Despite a large number of studies
      • Pozzi F.
      • Plummer H.A.
      • Shanley E.
      • et al.
      Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
      ,
      • Bullock G.S.
      • Faherty M.S.
      • Ledbetter L.
      • Thigpen C.A.
      • Sell T.C.
      Shoulder range of motion and baseball arm injuries: A systematic review and meta-analysis.
      and the widespread use of shoulder ROM in injury risk screening,
      • Wilk K.E.
      • Macrina L.C.
      • Fleisig G.S.
      • et al.
      Deficits in glenohumeral passive range of motion increase risk of shoulder injury in professional baseball pitchers: A prospective study.
      ,
      • Wilk K.E.
      • Macrina L.C.
      • Fleisig G.S.
      • et al.
      Deficits in glenohumeral passive range of motion increase risk of elbow injury in professional baseball pitchers: A prospective study.
      current findings are unclear as to whether there is a true cause-effect relation with baseball elbow injuries. In a 2018 meta-analysis of 3 studies, shoulder internal rotation and total rotation (external rotation plus internal rotation) were identified as injury prognostic factors whereas external rotation was not.
      • Bullock G.S.
      • Faherty M.S.
      • Ledbetter L.
      • Thigpen C.A.
      • Sell T.C.
      Shoulder range of motion and baseball arm injuries: A systematic review and meta-analysis.
      In contrast, a 2020 meta-analysis of 3 studies identified shoulder external rotation as an injury prognostic factor but found that shoulder internal rotation and total rotation were not injury prognostic factors.
      • Pozzi F.
      • Plummer H.A.
      • Shanley E.
      • et al.
      Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
      Since these publications, further studies have evaluated this issue, with conflicting results between clinical values of shoulder internal rotation, external rotation, total rotation, and shoulder flexion ROM.
      • Camp C.L.
      • Zajac J.M.
      • Pearson D.B.
      • et al.
      Decreased shoulder external rotation and flexion are greater predictors of injury than internal rotation deficits: Analysis of 132 pitcher-seasons in professional baseball.
      • Norton R.
      • Honstad C.
      • Joshi R.
      • Silvis M.
      • Chinchilli V.
      • Dhawan A.
      Risk factors for elbow and shoulder injuries in adolescent baseball players: A systematic review.
      • Stokes H.
      • Eaton K.
      • Zheng N.
      Shoulder external rotational properties during physical examination are associated with injury that requires surgery and shoulder joint loading during baseball pitching.
      Despite these extensive efforts, we continue to see conflicting findings that make it difficult to provide clear clinical recommendations.
      We argue that the conflicting findings revolving around the value of shoulder ROM measurements associated with baseball elbow injuries are the result of 4 gaps in the research approaches implemented to date: ambiguous research questions, mixed study populations, statistical models used, and shoulder ROM methodology. Specifically, there is a mismatch of methods, statistical models, and conclusions such as (1) investigating the association (i.e., correlation) between shoulder ROM measurements and injury and (2) investigating the cause-effect relation of shoulder ROM to baseball injuries. The purpose of this article is to detail the required scientific steps to evaluate whether preseason shoulder ROM is a potential causal factor for pitching elbow injury. We also provide recommendations to allow for future causal inferences to be made between shoulder ROM and elbow injury. This information will ultimately assist in informing clinical models of care and decision making for baseball throwers.

      Implications of Ambiguous Scientific Methods in Understanding Causal Relations

      Ambiguous scientific models and methods result in inaccurate conclusions and clinical recommendations with unintended clinical consequences. Unfortunately, these mistakes are repeated in multiple studies, leading to a “canonization of false facts”
      • Nissen S.B.
      • Magidson T.
      • Gross K.
      • Bergstrom C.T.
      Publication bias and the canonization of false facts.
      that are difficult to change in research and clinical practice. To provide a brief hypothetical example, ice cream consumption is associated with drowning. In reaction to these findings, the government bans ice cream sales. This action holds little hope of reducing the mortality rate because the causal factor is the sunny weather during the summer season that increases both the number of persons who swim in open water and the volume of ice cream eaten. Unfortunately for baseball clinicians, we are at a much earlier stage of our understanding and cannot yet infer any treatment strategies from studies that show an association between shoulder ROM measures and elbow injury because it is unclear whether there are other factors that explain the relation between shoulder ROM and elbow injury. Better research designs, such as assessing how humeral torsion affects clinical shoulder ROM measurements,
      • Noonan T.J.
      • Shanley E.
      • Bailey L.B.
      • et al.
      Professional pitchers with glenohumeral internal rotation deficit (GIRD) display greater humeral retrotorsion than pitchers without GIRD.
      can bring us closer to meaningful treatment inferences.
      The discussion of association versus causation has strong implications on clinical examinations, screening, and interventions. Understanding the potential true causal relations between shoulder ROM and medial elbow injury allows sports medicine clinicians to refocus on the important causative factors—and to discard irrelevant information. In a brief clinical example, much of the more recent baseball medical literature has discussed the importance of shoulder external rotation and flexion over shoulder internal rotation as important prognostic markers.
      • Camp C.L.
      • Zajac J.M.
      • Pearson D.B.
      • et al.
      Decreased shoulder external rotation and flexion are greater predictors of injury than internal rotation deficits: Analysis of 132 pitcher-seasons in professional baseball.
      In a hypothetical example, through careful causal study, shoulder external rotation was found to have a true strong positive causal relation to medial elbow injuries (i.e., greater external rotation increases elbow injury likelihood). Furthermore, shoulder internal rotation was not found to have a causal effect on elbow injuries. As a result, clinicians would provide interventions that focus on providing shoulder external rotation end-range stability and maintaining preseason external rotation levels.
      • Reinold M.M.
      • Gill T.J.
      • Wilk K.E.
      • Andrews J.R.
      Current concepts in the evaluation and treatment of the shoulder in overhead throwing athletes, part 2: Injury prevention and treatment.
      However, if shoulder internal rotation is identified as a causal factor, and external rotation is not, then the clinician will prescribe posterior shoulder manual therapy,
      • Bailey L.B.
      • Thigpen C.A.
      • Hawkins R.J.
      • Beattie P.F.
      • Shanley E.
      Effectiveness of manual therapy and stretching for baseball players with shoulder range of motion deficits.
      as well as daily posterior shoulder stretching,
      • Bailey L.B.
      • Thigpen C.A.
      • Hawkins R.J.
      • Beattie P.F.
      • Shanley E.
      Effectiveness of manual therapy and stretching for baseball players with shoulder range of motion deficits.
      ,
      • Reinold M.M.
      • Gill T.J.
      • Wilk K.E.
      • Andrews J.R.
      Current concepts in the evaluation and treatment of the shoulder in overhead throwing athletes, part 2: Injury prevention and treatment.
      and will discard shoulder external rotation end-range stability. Although this is only a brief and simple (and one would argue a reductionist) example, it demonstrates the importance of the minutia and semantics. Knowing the true causal structure allows clinicians to identify measures and interventions and focus their efforts to impact patient outcomes.

      Defining Association, Causality, and Prediction

      An “association” is defined as only a statistical relation between a factor (e.g., shoulder ROM) and an outcome (e.g., elbow injury).
      • Merlo J.
      • Ohlsson H.
      • Lynch K.F.
      • Chaix B.
      • Subramanian S.V.
      Individual and collective bodies: using measures of variance and association in contextual epidemiology.
      Associations do not account for other effects. Associations are valuable and provide the foundation for future hypotheses.
      • Altman D.G.
      • Lyman G.H.
      Methodological challenges in the evaluation of prognostic factors in breast cancer.
      Associations cannot (and should not) provide information regarding clinical intervention because there may be other reasons (factors) “why” associations exist.
      • Merlo J.
      • Ohlsson H.
      • Lynch K.F.
      • Chaix B.
      • Subramanian S.V.
      Individual and collective bodies: using measures of variance and association in contextual epidemiology.
      Many times, clinicians think of an association as solely a correlation; however, a correlation is a subset of associations. A correlation, although broadly accurate, can miss important details, as seen in the spurious example of ice cream and drowning. Closer to home, coaches and medical staff once believed that water consumption during exercise caused cramping owing to the observed correlation between athletes drinking and experiencing cramping. It transpired that those drinking more were at higher risk of heat illness and subsequent cramping. Restricting water consumption during practice increased the risk of cramping (and that of heat illness).
      • Kleiner S.M.
      Water: An essential but overlooked nutrient.
      “Causality” is the true cause-effect relation between a factor and an outcome.
      • Hernán M.A.
      • Hsu J.
      • Healy B.
      A second chance to get causal inference right: A classification of data science tasks.
      This relation is extremely sensitive to the effects of other factors (e.g., confounders) and other research biases (Table 1).
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Confounding and collapsibility in causal inference.
      Causal factors can be used to identify potential interventions.
      • Prosperi M.
      • Guo Y.
      • Sperrin M.
      • et al.
      Causal inference and counterfactual prediction in machine learning for actionable healthcare.
      Table 1Definitions
      TermDefinitionClinical Example
      AssociationA statistical relation between a factor and an event or outcome, which must have a specified direction and magnitude
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      People who drink coffee are more likely to receive a diagnosis of cancer.
      • Savitz D.A.
      • Barón A.E.
      Estimating and correcting for confounder misclassification.
      CausalityA certain factor contributes to (i.e., causes) an event if, without this factor, the event would not occur
      • Hernán M.A.
      • Hsu J.
      • Healy B.
      A second chance to get causal inference right: A classification of data science tasks.
      A bacterial infection causes a fever. Without the bacterial infection, no fever would appear.
      PredictionMathematical models (i.e., equations) that include multiple factors that forecast the risk or probability of an undiagnosed condition (diagnostic) or future outcome (prognostic)
      • Collins G.S.
      • Reitsma J.B.
      • Altman D.G.
      • Moons K.G.
      Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement.


      Prediction models include both causal and non-causal factors.
      The Framingham heart disease prediction model was developed to predict the risk of sustaining coronary cardiovascular disease.
      • Lloyd-Jones D.M.
      • Wilson P.W.
      • Larson M.G.
      • et al.
      Framingham risk score and prediction of lifetime risk for coronary heart disease.
      Secondary terms
       ConfounderA variable that is a common cause of the exposure (or factor) of interest and the outcome
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Confounding and collapsibility in causal inference.
      Coffee drinkers are more likely to smoke cigarettes. When cigarette consumption is controlled for as a confounder, there is no relation between coffee drinking and cancer.
      • Savitz D.A.
      • Barón A.E.
      Estimating and correcting for confounder misclassification.
       Prognostic factorA factor that is associated with a future clinical outcome in persons with a baseline disease, condition, or health state
      • Riley R.D.
      • Hayden J.A.
      • Steyerberg E.W.
      • et al.
      Prognosis Research Strategy (PROGRESS) 2: Prognostic factor research.


      Prognostic factors must precede the outcome and can be causal or non-causal but do not have to be biologically plausible.
      A high cholesterol level is a prognostic factor for myocardial infarction. A high cholesterol level precedes the outcome and is biologically plausible.
       Conditioning (also known as controlling or adjusting)Disconnecting a factor from its associations (or causal effects) between ≥2 other factors
      • Pearl J.
      Causality.
      ,
      • Morgan S.L.
      • Winship C.
      Counterfactuals and causal inference.


      This can be performed through stratification, sub-classification, or adjustment.
      Conditioning on the confounder humeral torsion blocks the backdoor path between shoulder range of motion, humeral torsion, and elbow injury. This allows the causal effect from shoulder range of motion and elbow injury to be assessed.
       Directed acyclic graphA graph that forms a directed path between a factor and an outcome to help clarify the potential causal relations

      This graph cannot have a continuous feedback loop.
      • Morgan S.L.
      • Winship C.
      Counterfactuals and causal inference.
      A DAG is presented in Figure 1.
      Causality must start with a specific causal question, which can be informed through the aid of a proposed (theoretical) causal model, drawn with directed acyclic graphs (DAGs) (Table 1, Fig 1). DAGs are pictorial models of a plausible underlying causal structure that consist of nodes (i.e., factors) and edges (i.e., arrows) that work unidirectionally.
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Causal diagrams for epidemiologic research.
      These diagrams can assist in identifying what factors to measure and control for when designing a study to answer a specific causal question.
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Causal diagrams for epidemiologic research.
      There is robust literature on DAGs,
      • Pearl J.
      Causality.
      ,
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Causal diagrams for epidemiologic research.
      ,
      • Krieger N.
      • Davey Smith G.
      The tale wagged by the DAG: Broadening the scope of causal inference and explanation for epidemiology.
      with many tutorials to further understand DAGs.
      • Hernán M.A.
      Causal diagrams: Draw your assumptions before your conclusions.
      ,
      • Digitale J.C.
      • Martin J.N.
      • Glymour M.M.
      Tutorial on directed acyclic graphs.
      Within the simple example in Figure 1, shoulder internal rotation ROM has a direct effect on medial elbow injuries, shown through the directed arrow from shoulder internal rotation to medial elbow injury. However, this effect is mitigated by the confounder of humeral torsion, shown with arrows stemming from humeral torsion to both shoulder internal rotation and medial elbow injury. Until humeral torsion is controlled for (also known as “conditioning” or “adjusting”) to block this specific effect on both shoulder internal rotation and medial elbow injury, the true direction and magnitude of the causal relation cannot be determined.
      Figure thumbnail gr1
      Fig 1Simple directed acyclic graph. Shoulder internal rotation range of motion (ROM) has a direct effect on medial elbow injuries, shown through the directed arrow. However, this effect is mitigated by the confounder humeral torsion, shown with arrows stemming from humeral torsion to both shoulder internal rotation and medial elbow injury. Until humeral torsion is controlled for (also known as “conditioning” or “adjusting”) to block the effect on both shoulder internal rotation and medial elbow injury, the true direction and magnitude of the causal relation cannot be determined. It is important to note that this directed acyclic graph infers 2 hypotheses that can be tested with observational studies: Humeral torsion should be associated with both shoulder internal rotational ROM and medial elbow injury, and shoulder internal rotation ROM should be associated with medial elbow injury. Regression analyses, for example, can give weights to these associations, along with statistical significance.

      Required Methods to Evaluate Whether Preseason Shoulder ROM Is a Causal Factor for Medial Elbow Injuries in Baseball Players

      To investigate whether preseason shoulder ROM is truly a causal factor for medial elbow injuries, specific methods are needed within the context of sport and baseball. The gold standard for assessing causality is the randomized controlled trial (RCT).
      • Hariton E.
      • Locascio J.J.
      Randomised controlled trials—The gold standard for effectiveness research.
      However, RCTs are difficult to perform and may be unethical in the sports setting for many reasons. Adherence or uptake could be limited because competing sports clubs may not want to contend with being randomized into the “control” group, potentially losing a competitive advantage.

      Bullock GS, Ward P, Peters S, et al. Call for open science in sports medicine [published online June 9, 2022]. Br J Sports Med. https://doi.org/10.1136/bjsports-2022-105719.

      To assess specific causal questions about injuries, it is unethical to randomize athletes into “hurt” and “healthy” groups.
      • Cook C.E.
      • Thigpen C.A.
      Five good reasons to be disappointed with randomized trials.
      It would be impractically difficult to randomize athletes into groups to have more or less shoulder ROM, providing further infeasibility of the RCT design.
      Previous research has identified arm injury prevention programs as effective in mitigating injury risk in baseball players.
      • Shanley E.
      • Kissenberth M.J.
      • Thigpen C.A.
      • et al.
      Preseason shoulder range of motion screening as a predictor of injury among youth and adolescent baseball pitchers.
      ,
      • Sakata J.
      • Nakamura E.
      • Suzuki T.
      • et al.
      Efficacy of a prevention program for medial elbow injuries in youth baseball players.
      As a result, it is unethical to not provide arm injury prevention programs to all study participants. Although a hybrid RCT (e.g., wedge design) is a viable method to overcome this obstacle,
      • Hussey M.A.
      • Hughes J.P.
      Design and analysis of stepped wedge cluster randomized trials.
      this still does not evaluate shoulder ROM as a causal factor in relation to baseball elbow injuries. This is because injury prevention programs must provide interventions on both shoulder ROM and strength to be ethically sound.
      • Shanley E.
      • Kissenberth M.J.
      • Thigpen C.A.
      • et al.
      Preseason shoulder range of motion screening as a predictor of injury among youth and adolescent baseball pitchers.
      ,
      • Sakata J.
      • Nakamura E.
      • Suzuki T.
      • et al.
      Efficacy of a prevention program for medial elbow injuries in youth baseball players.
      Consequentially, intervention programs can investigate an injury prevention program’s effectiveness in reducing arm injuries but cannot isolate shoulder ROM (or shoulder strength) as a causal factor.
      Because RCTs are not a true option to answer this important question, one must rely on prospective observational studies (Table 2). It should be noted that using this methodology does not imply that these questions are unanswerable or that these potential conclusions should have less weight in their scientific foundation. If prospective observational studies are carefully designed,
      • Hernán M.A.
      • Robins J.M.
      Using big data to emulate a target trial when a randomized trial is not available.
      with adequate (large) sample sizes, causal questions can be answered with robust scientific rigor.
      • Hernán M.A.
      The C-word: Scientific euphemisms do not improve causal inference from observational data.
      These studies must require careful controlling of confounders
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Confounding and collapsibility in causal inference.
      and other biases (e.g., data missingness,
      • Sterne J.A.
      • White I.R.
      • Carlin J.B.
      • et al.
      Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls.
      misclassification,
      • Espeland M.A.
      • Hui S.L.
      A general approach to analyzing epidemiologic data that contain misclassification errors.
      immortal time,
      • Lévesque L.E.
      • Hanley J.A.
      • Kezouh A.
      • Suissa S.
      Problem of immortal time bias in cohort studies: Example using statins for preventing progression of diabetes.
      and measurement error
      • Innes G.K.
      • Bhondoekhan F.
      • Lau B.
      • Gross A.L.
      • Ng D.K.
      • Abraham A.G.
      The measurement error elephant in the room: Challenges and solutions to measurement error in epidemiology.
      ) to truly investigate the potential for a causal relation. To account for the multitude of confounders and biases, researchers need to develop “scientific models before statistical models.”
      • McElreath R.
      Statistical rethinking: A Bayesian course with examples in R and Stan.
      In other words, one must design a plausible causal model and identify methods to control for appropriate confounders and biases before proceeding with data collection or statistical analyses.
      • McElreath R.
      Statistical rethinking: A Bayesian course with examples in R and Stan.
      This “scientific model” should ideally be constructed through an appropriate causal DAG.
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      ,
      • Hernán M.A.
      • Robins J.M.
      Instruments for causal inference: An epidemiologist's dream?.
      The reason behind this scientific methodology is that statistical analyses alone cannot decipher causal relations.
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Confounding and collapsibility in causal inference.
      ,
      • McElreath R.
      Statistical rethinking: A Bayesian course with examples in R and Stan.
      Although beyond the scope of this article, arbitrarily controlling for different variables (e.g., stepwise regression
      • Steyerberg E.W.
      • Eijkemans M.J.
      • Habbema J.D.F.
      Stepwise selection in small data sets: A simulation study of bias in logistic regression analysis.
      ) can bias the results by either artificially inflating,
      • Cole S.R.
      • Platt R.W.
      • Schisterman E.F.
      • et al.
      Illustrating bias due to conditioning on a collider.
      decreasing,
      • Pearce N.
      • Richiardi L.
      Commentary: Three worlds collide: Berkson’s bias, selection bias and collider bias.
      or completely nullifying
      • Schisterman E.F.
      • Cole S.R.
      • Platt R.W.
      Overadjustment bias and unnecessary adjustment in epidemiologic studies.
      the true causal (or non-causal) relation. Furthermore, the inclusion of “all measured factors” or a set of attainable factors does not assess causal relations but assesses only associations.
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      ,
      • Hernán M.A.
      • Robins J.M.
      Instruments for causal inference: An epidemiologist's dream?.
      To navigate these statistical biases, one must use prior scientific, clinical, and biological knowledge to construct the DAG.
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      ,
      • Hernán M.A.
      • Robins J.M.
      Instruments for causal inference: An epidemiologist's dream?.
      Different hypotheses can then be systematically tested to decipher the causal structure, and the causal structure can be refined over time.
      Table 2Positive and Negative Implications for Methods to Assess Causal Relations in Sport
      MethodPositive ImplicationsNegative Implications
      Randomized controlled trialPutative allocation of treatment and control

      Clear identification of causal effects
      Not generalizable

      Possibly infeasible in sport (i.e., competitive advantage)

      Possibly unethical (i.e., athletes cannot be randomly injured)
      Observational causal inferenceGeneralizable

      Ethical and feasible
      Requirement for massive amount of data

      Unobserved confounding, which may not allow control of all important confounders

      Possible lack of consensus regarding “true” causal structure

      Modifiable Factors in Future Studies of Shoulder ROM

      So, where does baseball elbow injury research go from here? First, we must clearly define the purpose of future baseball injury studies. Are associations, prediction, or causality being investigated? As seen within this article, these are different scientific constructs (all valid and important aims) with different scientific methodologies and clinical implications.
      • Merlo J.
      • Ohlsson H.
      • Lynch K.F.
      • Chaix B.
      • Subramanian S.V.
      Individual and collective bodies: using measures of variance and association in contextual epidemiology.
      If one is trying to understand causality, one must start with the specific causal question that is defined through a causal DAG. Although beyond the scope of this article, it should be highlighted that not all factors may be collected and included in a DAG and that DAGs change for each specific causal question.
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      ,
      • Savitz D.A.
      • Barón A.E.
      Estimating and correcting for confounder misclassification.
      ,
      • McElreath R.
      Statistical rethinking: A Bayesian course with examples in R and Stan.
      ,
      • Hernán M.A.
      • Robins J.M.
      Instruments for causal inference: An epidemiologist's dream?.
      Owing to the intricacies in designing causal structures, we propose a DAG as a scientific model to assess the causal effect of preseason shoulder internal rotation ROM on medial elbow injuries in professional baseball players in the first 3 months of baseball spring training and the season (Fig 2). The reader will notice that the causal model is extremely specific as subtle changes in the scientific question can have ramifications on the causal structure. We chose this specific causal model because static models are less intricate compared with temporal dynamic models (also known as “time-varying confounding”).
      • Mansournia M.A.
      • Etminan M.
      • Danaei G.
      • Kaufman J.S.
      • Collins G.
      Handling time varying confounding in observational research.
      Furthermore, shoulder internal rotation ROM was chosen because this is the most assessed prognostic factor identified in relation to baseball elbow injuries.
      • Reinold M.M.
      • Gill T.J.
      • Wilk K.E.
      • Andrews J.R.
      Current concepts in the evaluation and treatment of the shoulder in overhead throwing athletes, part 2: Injury prevention and treatment.
      The period of the first 3 months after measurement is clearly defined because shoulder ROM may change throughout the season,
      • Reinold M.M.
      • Wilk K.E.
      • Macrina L.C.
      • et al.
      Changes in shoulder and elbow passive range of motion after pitching in professional baseball players.
      causing a degradation in the relation between initial preseason shoulder ROM measurements and middle- or late-season arm injuries.
      • Shanley E.
      • Thigpen C.A.
      • Collins G.S.
      • et al.
      Including modifiable and nonmodifiable factors improves injury risk assessment in professional baseball pitchers.
      We would like to highlight that this proposed causal DAG is not the final model: We invite others to comment on and improve this DAG. Cumulatively, this effort will advance our understanding of the potential causal systems between shoulder ROM and baseball elbow injuries.
      Figure thumbnail gr2
      Fig 2Suggested initial directed acyclic graph (DAG). This complex DAG is our suggestion on the causal structure between preseason shoulder internal range of motion (ROM) and medial elbow injury in professional baseball pitchers within the first 3 months of data collection (i.e., spring training and the first month of the season). It should be noted that, in accordance with Pearl,
      • Pearl J.
      Causality.
      the “tissue overload” node has been hidden as the causal pathway—in agreement with the model of Windt and Gabbett
      • Windt J.
      • Gabbett T.J.
      How do training and competition workloads relate to injury? The workload—Injury aetiology model.
      —and this node is transitive to medial elbow injury in relation to all other factors. As reported by Hernán and Robins,
      • Hernán M.A.
      • Robins J.M.
      Causal inference: What if?.
      a DAG only needs to represent the minimal nodes to reflect structural confounding to block all backdoor paths. Pitching biomechanical nodes are not reported because they do not relate to backdoor paths within this DAG. (ER, external rotation; IR, internal rotation.)
      Although a full discussion of the presented DAG is beyond the scope of this article, it is pertinent to orient the reader to the main DAG highlights. This causal DAG has been created for an ideal scenario, with all factors observable and collected. The proposed causal factor, shoulder internal rotation, has a direct effect on medial elbow injuries, with a secondary pathway to elbow varus torque to medial elbow injury. There are multiple strategies for factor conditioning to block all confounding (i.e., close all backdoor paths).
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Confounding and collapsibility in causal inference.
      ,
      • Greenland S.
      • Pearl J.
      • Robins J.M.
      Causal diagrams for epidemiologic research.
      To evaluate the total causal effect, the potential conditioning scenarios are as follows:
      • Humeral torsion, individual genetic frailty, previous shoulder injury, shoulder external rotation–internal rotation strength
      • Humeral torsion, previous shoulder injury, shoulder external rotation–internal rotation strength, tissue quality
      To evaluate only the direct effect, the potential conditioning scenarios are as follows:
      • Elbow varus torque, fatigue, individual genetic frailty, pitching velocity
      • Elbow varus torque, individual genetic frailty, pitching velocity, previous shoulder injury, shoulder external rotation–internal rotation strength
      • Elbow varus torque, individual genetic frailty, training and competition load
      We would like to draw the reader’s attention to shoulder external rotation and shoulder flexion ROM. Within this DAG, these should not be conditioned on (i.e. controlled for) because they induce what is termed “collider bias.”
      • Cole S.R.
      • Platt R.W.
      • Schisterman E.F.
      • et al.
      Illustrating bias due to conditioning on a collider.
      This occurs when multiple arrows are “pointed” to a node with other paths closed. Collider bias opens up further confounding and actually biases the causal effect.
      • Cole S.R.
      • Platt R.W.
      • Schisterman E.F.
      • et al.
      Illustrating bias due to conditioning on a collider.
      ,
      • Pearce N.
      • Richiardi L.
      Commentary: Three worlds collide: Berkson’s bias, selection bias and collider bias.

      Conclusions

      Until we clearly define what the scientific question is (association, causal, or prediction), there will continue to be confusion on the potential prognostic and causal relations between shoulder ROM and arm injuries in baseball players. Although RCTs are the gold standard in causal research, these methodologic designs may not be feasible or ethical in the sports setting. Prospective observational studies are required, with careful controlling of confounding and biases to discern true causal relations. Scientific models need to be clearly and precisely defined prior to data collection and statistical analyses because data and analyses alone cannot decipher causal relations. To jump-start this process, we have proposed a causal DAG for shoulder internal rotation ROM as a cause-effect relation to medial elbow injuries in professional baseball pitchers. The details of portions of this model can be validated with piecemeal observational studies, which will provide weights between individual nodes. We invite other experts, through open commentary, to help improve this DAG. Although some readers may believe that this discussion is merely scientific semantics, these issues, until clearly defined and executed, will continue to inhibit clinical examinations and impact throwing-arm health.

      Supplementary Data

      References

        • Bullock G.S.
        • Uhan J.
        • Harriss E.K.
        • Arden N.K.
        • Filbay S.R.
        The relationship between baseball participation and health: A systematic scoping review.
        J Orthop Sports Phys Ther. 2020; 50: 55-66
        • Posner M.
        • Cameron K.L.
        • Wolf J.M.
        • Belmont Jr., P.J.
        • Owens B.D.
        Epidemiology of Major League Baseball injuries.
        Am J Sports Med. 2011; 39: 1675-1691
        • Conte S.
        • Camp C.L.
        • Dines J.S.
        Injury trends in Major League Baseball over 18 seasons: 1998-2015.
        Am J Orthop. 2016; 45: 116-123
        • Camp C.L.
        • Conte S.
        • D'Angelo J.
        • Fealy S.A.
        Epidemiology of ulnar collateral ligament reconstruction in Major and Minor League Baseball pitchers: Comprehensive report of 1429 cases.
        J Shoulder Elbow Surg. 2018; 27: 871-878
        • Boltz A.J.
        • Powell J.R.
        • Robison H.J.
        • Morris S.N.
        • Collins C.L.
        • Chandran A.
        Epidemiology of injuries in National Collegiate Athletic Association men's baseball: 2014–2015 through 2018–2019.
        J Athl Train. 2021; 56: 742-749
        • Meldau J.E.
        • Srivastava K.
        • Okoroha K.R.
        • Ahmad C.S.
        • Moutzouros V.
        • Makhni E.C.
        Cost analysis of Tommy John surgery for Major League Baseball teams.
        J Shoulder Elbow Surg. 2020; 29: 121-125
        • Peters S.D.
        • Bullock G.S.
        • Goode A.P.
        • Garrigues G.E.
        • Ruch D.S.
        • Reiman M.P.
        The success of return to sport after ulnar collateral ligament injury in baseball: A systematic review and meta-analysis.
        J Shoulder Elbow Surg. 2018; 27: 561-571
      1. Lobb NJ, Lu Z, Long E, Chow K, Michener LA. Sonographic morphological and qualitative deficits in the elbow ulnar collateral ligament and ulnohumeral joint in throwing arms of asymptomatic collegiate baseball pitchers. Skeletal Radiol 2023;52:31-37.

        • Pozzi F.
        • Plummer H.A.
        • Shanley E.
        • et al.
        Preseason shoulder range of motion screening and in-season risk of shoulder and elbow injuries in overhead athletes: Systematic review and meta-analysis.
        Br J Sports Med. 2020; 54: 1019-1027
        • Shanley E.
        • Rauh M.J.
        • Michener L.A.
        • Ellenbecker T.S.
        • Garrison J.C.
        • Thigpen C.A.
        Shoulder range of motion measures as risk factors for shoulder and elbow injuries in high school softball and baseball players.
        Am J Sports Med. 2011; 39: 1997-2006
        • Erickson B.J.
        The epidemic of Tommy John surgery: The role of the orthopedic surgeon.
        Am J Orthop (Belle Mead NJ). 2015; 44: E36-E37
        • Shanley E.
        • Kissenberth M.J.
        • Thigpen C.A.
        • et al.
        Preseason shoulder range of motion screening as a predictor of injury among youth and adolescent baseball pitchers.
        J Shoulder Elbow Surg. 2015; 24: 1005-1013
        • Sakata J.
        • Nakamura E.
        • Suzuki T.
        • et al.
        Efficacy of a prevention program for medial elbow injuries in youth baseball players.
        Am J Sports Med. 2018; 46: 460-469
        • Bullock G.S.
        • Faherty M.S.
        • Ledbetter L.
        • Thigpen C.A.
        • Sell T.C.
        Shoulder range of motion and baseball arm injuries: A systematic review and meta-analysis.
        J Athl Train. 2018; 53: 1190-1199
        • Wilk K.E.
        • Macrina L.C.
        • Fleisig G.S.
        • et al.
        Deficits in glenohumeral passive range of motion increase risk of shoulder injury in professional baseball pitchers: A prospective study.
        Am J Sports Med. 2015; 43: 2379-2385
        • Wilk K.E.
        • Macrina L.C.
        • Fleisig G.S.
        • et al.
        Deficits in glenohumeral passive range of motion increase risk of elbow injury in professional baseball pitchers: A prospective study.
        Am J Sports Med. 2014; 42: 2075-2081
        • Bailey L.B.
        • Shanley E.
        • Hawkins R.
        • et al.
        Mechanisms of shoulder range of motion deficits in asymptomatic baseball players.
        Am J Sports Med. 2015; 43: 2783-2793
        • Bailey L.B.
        • Thigpen C.A.
        • Hawkins R.J.
        • Beattie P.F.
        • Shanley E.
        Effectiveness of manual therapy and stretching for baseball players with shoulder range of motion deficits.
        Sports Health. 2017; 9: 230-237
        • Camp C.L.
        • Zajac J.M.
        • Pearson D.B.
        • et al.
        Decreased shoulder external rotation and flexion are greater predictors of injury than internal rotation deficits: Analysis of 132 pitcher-seasons in professional baseball.
        Arthroscopy. 2017; 33: 1629-1636
        • Norton R.
        • Honstad C.
        • Joshi R.
        • Silvis M.
        • Chinchilli V.
        • Dhawan A.
        Risk factors for elbow and shoulder injuries in adolescent baseball players: A systematic review.
        Am J Sports Med. 2019; 47: 982-990
        • Stokes H.
        • Eaton K.
        • Zheng N.
        Shoulder external rotational properties during physical examination are associated with injury that requires surgery and shoulder joint loading during baseball pitching.
        Am J Sports Med. 2021; 49: 3647-3655
        • Nissen S.B.
        • Magidson T.
        • Gross K.
        • Bergstrom C.T.
        Publication bias and the canonization of false facts.
        Elife. 2016; 5e21451
        • Noonan T.J.
        • Shanley E.
        • Bailey L.B.
        • et al.
        Professional pitchers with glenohumeral internal rotation deficit (GIRD) display greater humeral retrotorsion than pitchers without GIRD.
        Am J Sports Med. 2015; 43: 1448-1454
        • Reinold M.M.
        • Gill T.J.
        • Wilk K.E.
        • Andrews J.R.
        Current concepts in the evaluation and treatment of the shoulder in overhead throwing athletes, part 2: Injury prevention and treatment.
        Sports Health. 2010; 2: 101-115
        • Merlo J.
        • Ohlsson H.
        • Lynch K.F.
        • Chaix B.
        • Subramanian S.V.
        Individual and collective bodies: using measures of variance and association in contextual epidemiology.
        J Epidemiol Comm Health. 2009; 63: 1043-1048
        • Altman D.G.
        • Lyman G.H.
        Methodological challenges in the evaluation of prognostic factors in breast cancer.
        in: Prognostic variables in node-negative and node-positive breast cancer. Springer, New York1998: 379-393
        • Steger J.
        Hydrating athletes then and now.
        (Published 2018. Accessed August 30, 2022.)
        • Kleiner S.M.
        Water: An essential but overlooked nutrient.
        J Am Diet Assoc. 1999; 99: 200-206
        • Hernán M.A.
        • Hsu J.
        • Healy B.
        A second chance to get causal inference right: A classification of data science tasks.
        Chance. 2019; 32: 42-49
        • Greenland S.
        • Pearl J.
        • Robins J.M.
        Confounding and collapsibility in causal inference.
        Stat Sci. 1999; 14: 29-46
        • Pearl J.
        Causality.
        Cambridge University Press, Cambridge2009
        • Hernán M.A.
        • Robins J.M.
        Causal inference: What if?.
        Chapman Hall Press, Boca Raton2022
        • Savitz D.A.
        • Barón A.E.
        Estimating and correcting for confounder misclassification.
        Am J Epidemiol. 1989; 129: 1062-1071
        • Collins G.S.
        • Reitsma J.B.
        • Altman D.G.
        • Moons K.G.
        Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement.
        Circulation. 2015; 131: 211-219
        • Lloyd-Jones D.M.
        • Wilson P.W.
        • Larson M.G.
        • et al.
        Framingham risk score and prediction of lifetime risk for coronary heart disease.
        Am J Cardiol. 2004; 94: 20-24
        • Riley R.D.
        • Hayden J.A.
        • Steyerberg E.W.
        • et al.
        Prognosis Research Strategy (PROGRESS) 2: Prognostic factor research.
        PLoS Med. 2013; 10e1001380
        • Morgan S.L.
        • Winship C.
        Counterfactuals and causal inference.
        Cambridge University Press, Cambridge2015
        • Prosperi M.
        • Guo Y.
        • Sperrin M.
        • et al.
        Causal inference and counterfactual prediction in machine learning for actionable healthcare.
        Nat Mach Intell. 2020; 2: 369-375
        • Greenland S.
        • Pearl J.
        • Robins J.M.
        Causal diagrams for epidemiologic research.
        Epidemiology. 1999; : 37-48
        • Krieger N.
        • Davey Smith G.
        The tale wagged by the DAG: Broadening the scope of causal inference and explanation for epidemiology.
        Int J Epidemiol. 2016; 45: 1787-1808
        • Hernán M.A.
        Causal diagrams: Draw your assumptions before your conclusions.
        • Digitale J.C.
        • Martin J.N.
        • Glymour M.M.
        Tutorial on directed acyclic graphs.
        J Clin Epidemiol. 2022; 142: 264-267
        • Hariton E.
        • Locascio J.J.
        Randomised controlled trials—The gold standard for effectiveness research.
        BJOG. 2018; 125: 1716
      2. Bullock GS, Ward P, Peters S, et al. Call for open science in sports medicine [published online June 9, 2022]. Br J Sports Med. https://doi.org/10.1136/bjsports-2022-105719.

        • Cook C.E.
        • Thigpen C.A.
        Five good reasons to be disappointed with randomized trials.
        J Man Manip Ther. 2019; 27: 63-65
        • Hussey M.A.
        • Hughes J.P.
        Design and analysis of stepped wedge cluster randomized trials.
        Contemp Clin Trials. 2007; 28: 182-191
        • Hernán M.A.
        • Robins J.M.
        Using big data to emulate a target trial when a randomized trial is not available.
        Am J Epidemiol. 2016; 183: 758-764
        • Hernán M.A.
        The C-word: Scientific euphemisms do not improve causal inference from observational data.
        Am J Public Health. 2018; 108: 616-619
        • Sterne J.A.
        • White I.R.
        • Carlin J.B.
        • et al.
        Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls.
        BMJ. 2009; 338: b2393
        • Espeland M.A.
        • Hui S.L.
        A general approach to analyzing epidemiologic data that contain misclassification errors.
        Biometrics. 1987; 43: 1001-1012
        • Lévesque L.E.
        • Hanley J.A.
        • Kezouh A.
        • Suissa S.
        Problem of immortal time bias in cohort studies: Example using statins for preventing progression of diabetes.
        BMJ. 2010; 340: b5087
        • Innes G.K.
        • Bhondoekhan F.
        • Lau B.
        • Gross A.L.
        • Ng D.K.
        • Abraham A.G.
        The measurement error elephant in the room: Challenges and solutions to measurement error in epidemiology.
        Epidemiol Rev. 2021; 43: 94-105
        • McElreath R.
        Statistical rethinking: A Bayesian course with examples in R and Stan.
        Chapman & Hall/CRC, London2020
        • Hernán M.A.
        • Robins J.M.
        Instruments for causal inference: An epidemiologist's dream?.
        Epidemiology. 2006; : 360-372
        • Steyerberg E.W.
        • Eijkemans M.J.
        • Habbema J.D.F.
        Stepwise selection in small data sets: A simulation study of bias in logistic regression analysis.
        J Clin Epidemiol. 1999; 52: 935-942
        • Cole S.R.
        • Platt R.W.
        • Schisterman E.F.
        • et al.
        Illustrating bias due to conditioning on a collider.
        Int J Epidemiol. 2010; 39: 417-420
        • Pearce N.
        • Richiardi L.
        Commentary: Three worlds collide: Berkson’s bias, selection bias and collider bias.
        Int J Epidemiol. 2014; 43: 521-524
        • Schisterman E.F.
        • Cole S.R.
        • Platt R.W.
        Overadjustment bias and unnecessary adjustment in epidemiologic studies.
        Epidemiology. 2009; 20: 488
        • Windt J.
        • Gabbett T.J.
        How do training and competition workloads relate to injury? The workload—Injury aetiology model.
        Br J Sports Med. 2017; 51: 428-435
        • Mansournia M.A.
        • Etminan M.
        • Danaei G.
        • Kaufman J.S.
        • Collins G.
        Handling time varying confounding in observational research.
        BMJ. 2017; 359: j4587
        • Reinold M.M.
        • Wilk K.E.
        • Macrina L.C.
        • et al.
        Changes in shoulder and elbow passive range of motion after pitching in professional baseball players.
        Am J Sports Med. 2008; 36: 523-527
        • Shanley E.
        • Thigpen C.A.
        • Collins G.S.
        • et al.
        Including modifiable and nonmodifiable factors improves injury risk assessment in professional baseball pitchers.
        J Orthop Sports Phys Ther. 2022; 52: 630-640