Shoulder Range of Motion Measurements and Baseball Elbow Injuries: Ambiguity in Scientific Models, Approach, and Execution is Hurting Overhead Athlete Health
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".