Incidence and risk factors for musculoskeletal disorders of the elbow in baseball pitchers: a systematic review of the literature.
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence and risk factors of musculoskeletal disorders of the elbow in baseball pitchers. DESIGN: Systematic review. DATA SOURCES: Medline, CINAHL, Cochrane, PubMed and SportDiscus from onset to July 7, 2018. ELIGIBILITY CRITERIA: Eligible studies included randomized controlled trials, cohort studies and case-control studies. Independent pairs of reviewers screened titles and abstracts for eligibility. Relevant articles were critically appraised for internal validity using the SIGN criteria. We included low risk of bias studies in our best evidence synthesis. RESULTS: We retrieved 4502 articles, 39 were critically appraised and nine had a low risk of bias. These were included in the evidence synthesis. The incidence of musculoskeletal disorders of the elbow ranges from 2.3% in adolescent pitchers to 40.6% in youth pitchers. Evidence suggests that pitch characteristics, inadequate rest, biomechanical and anthropometric factors may be risk factors of UCL tears. SUMMARY/CONCLUSION: Baseball pitchers develop musculoskeletal disorders of the elbow. There is little high-quality evidence to understand the etiology. Preliminary evidence suggests the risk factors are multifactorial.PROSPERO Trial Registration Number: CRD42018092081.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".