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Record W4415524796 · doi:10.1136/bjsports-2025-109907

Prevention strategies and modifiable risk factors for upper extremity injury: a systematic review and meta-analysis for the Female, woman and/or girl Athlete Injury pRevention (FAIR) consensus

2025· review· en· W4415524796 on OpenAlexaff
Emily E Heming, Eric Gibson, Kenzie B. Friesen, Chelsea Martin, Maitland Martin, Martin Asker, Cheri Blauwet, Garrett S. Bullock, Hilde Fredriksen, Jean‐Michel Galarneau, Alix Hayden, Jae Hyung Lee, A. Mosler, Grethe Myklebust, Babette M Pluim, Jane S Thornton, Jackie L. Whittaker, Rod Whiteley, Kay M. Crossley, Merete Møller, Carolyn A. Emery

Bibliographic record

VenueBritish Journal of Sports Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of British ColumbiaWestern UniversityCanadian Rural Health Research SocietyUniversity of SaskatchewanArthritis Research Centre of CanadaUniversity of Calgary
FundersInternational Olympic Committee
KeywordsGirlAthletesMEDLINEInjury preventionPoison controlOccupational safety and healthRisk factor

Abstract

fetched live from OpenAlex

Objectives To examine injury prevention strategies and potentially modifiable risk factors (MRFs) for upper extremity (UE) injuries in female, woman and/or girl athletes (female/woman/girl). Design Systematic review with meta-analysis, semiquantitative analyses and Grading of Recommendations Assessment, Development and Evaluation in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Data sources MEDLINE (Medical Literature Analysis and Retrieval System Online), CINAHL (Cumulative Index to Nursing and Allied Health Literature), APA PsycINFO (American Psychological Association Psychological Information Database), SPORTDiscus (Sports Discus Database), EMBASE (Excerpta Medica Database), and ERIC (Education Resources Information Center) (30 October 2023) and Cochrane Systematic Review Database and the Cochrane Central Register of Controlled Trials (CENTRAL) (25 November 2023). Eligibility Primary data studies with comparison group(s) assessing the association of prevention strategies and/or MRFs for sport-related UE injury, with ≥1 female/woman/girl in each study group. Results 55 studies (n=20 intervention, n=35 MRF) were included with 33 228 athletes (8642 female/woman/girl; 26%). Of these, 17 (31%) reported female/woman/girl-specific estimates and included five injury locations (n=3 general UE, n=12 shoulder, n=3 elbow, n=3 wrist/hand). One prevention strategy (n=5 shoulder-specific exercise programmes) and seven MRFs were identified, including less range of motion (n=6), less shoulder muscle strength (n=8), high training load (n=1), presence of scapular dyskinesis (n=3), high sport specialisation (n=2), equipment differences (n=1) and less sport-specific conditioning (n=1). Pooled data from three studies suggest that shoulder exercise programmes consisting of strength, stability/control and sport-specific exercises reduce shoulder injury rates by 51% (95% CI 0.30 to 079; I 2 0.0%; very-low certainty evidence) across paediatric (≤18 years) and adult handball and volleyball players. Conclusions Our understanding of female/woman/girl UE injury prevention is limited by heterogeneity across injury outcomes, interventions, MRFs and limited female/woman/girl athlete-specific data. Shoulder-specific strengthening and stability exercise programmes may be beneficial to reduce shoulder injury rates in female/woman/girl handball and volleyball players. Future research should prioritise female/woman/girl athletes to reduce the burden of UE injuries. PROSPERO registration number PROSPERO CRD42024494967.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.058
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0220.037
Bibliometrics0.0100.006
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.068
GPT teacher head0.371
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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
Published2025
Admission routes1
Has abstractyes

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