Increased pitch velocity and workload are common risk factors for ulnar collateral ligament injury in baseball players: a systematic review
Bibliographic record
Abstract
Importance Ulnar collateral ligament (UCL) injuries commonly occur in baseball players. Strategies for injury prevention have long been accepted without clinical data informing which risk factors lead to serious injury. Objective The objective of this study was to systematically review the impact of various pitching-related risk factors for UCL injury in baseball players from all levels of play. Evidence review The electronic databases MEDLINE, EMBASE and PubMed were systematically searched until 4 March 2018, and pertinent data were abstracted by two independent reviewers. Search terms included ‘ulnar collateral ligament’, ‘medial ulnar collateral ligament’, ‘Tommy John’, ‘risk’ and ‘association’. Inclusion criteria were English-language studies, level of evidence I–IV and studies reporting risk factors for UCL injury of the elbow in baseball players. Study quality was assessed using the methodological index for non-randomised studies (MINORS) criteria. The results are presented in a narrative summary. Findings Pitching practices (workload and pitch characteristics) were reported in 9/15 studies. Specifically, three of four studies (n=1810) reported increased pitch workload as a risk factor for native UCL injury (p<0.001 to 0.02). The most common pitch characteristic reported was pitch velocity with four of five studies showing increased velocity being significantly associated with native UCL injury (p<0.01 to 0.02). Biomechanical risk factors reported were increased humeral retrotorsion (two studies; n=324), poor lower extremity and trunk balance (one study; n=42) and loss of total arc of shoulder motion (two studies; n=118), all significantly associated with UCL injury (p<0.0001 to 0.05). One of three studies assessing pitch workload as a risk factor for re-rupture of UCL reconstruction found a significant association (p<0.01). Conclusions and relevance Pitching practices, reflected by increased pitch workload and velocity, were most commonly associated with UCL injury; however, the definition of workload (number of pitches per game, inning or season) was inconsistently reported. Biomechanical risk factors were less commonly reported and lack sufficient evidence to recommend preventative strategies. More quality data is needed to refine the current recommendations for injury prevention in baseball players. Level of evidence III.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".