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Record W2112746076 · doi:10.1136/bjsm.2002.003079

Strategies for prevention of soccer related injuries: a systematic review

2004· review· en· W2112746076 on OpenAlexaff
Lise Olsen, Aaron T. Scanlan, M MacKay, Shelina Babul, D H Reid, Marianne Clark, Parminder Raina

Bibliographic record

VenueBritish Journal of Sports Medicine · 2004
Typereview
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster UniversityChildren's Hospital of Eastern OntarioSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsRelevance (law)Intervention (counseling)Inclusion (mineral)MedicineQuality (philosophy)Evidence-based medicineEvidence-based practiceSystematic reviewMedical educationMEDLINEPsychologyAlternative medicineApplied psychologyNursingPathologyPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine evidence on the effectiveness of current injury prevention strategies in soccer, determine the applicability of the evidence to children and youth, and make recommendations on policy, programming, and future research. METHODS: Standard systematic review methodology was modified and adopted for this review. Research questions and relevance criteria were developed a priori. Potentially relevant studies were located through electronic and hand searches. Articles were assessed for relevance and quality by two independent assessors, and the results of relevant articles were abstracted and synthesised. RESULTS: A total of 44 potentially relevant articles from electronic (n = 37) and hand (n = 7) searches yielded four that met inclusion criteria. These four studies addressed a range of intervention strategies and varied with respect to results and quality of evidence. CONCLUSIONS: Some of the strategies look promising but lack adequate evaluation or require further research among younger players. Practice, policy, and research recommendations are provided as a result of the synthesis.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.325
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.364
Teacher spread0.336 · 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.

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

Citations66
Published2004
Admission routes1
Has abstractyes

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