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Record W3125764319

Incidence and risk factors for musculoskeletal disorders of the elbow in baseball pitchers: a systematic review of the literature.

2020· review· en· W3125764319 on OpenAlexaff
Chris Grant, Taylor Tuff, Melissa Corso, James J. Young, Paula Stern, Elie Côté, Pierre Côté

Bibliographic record

VenuePubMed · 2020
Typereview
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsCentre for Disability Prevention and RehabilitationOntario Tech UniversityUniversity of TorontoCanadian Memorial Chiropractic College
Fundersnot available
KeywordsMedicineCINAHLIncidence (geometry)MEDLINEPhysical therapyPsychological interventionPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.100
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.281
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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