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Record W1147948371 · doi:10.1177/0009922815602631

A 20-Year Comparison of Football-Related Injuries in American and Canadian Youth Aged 6 to 17 Years

2015· article· en· W1147948371 on OpenAlexaffabout
Glenn Keays, Debbie Friedman, Isabelle Gagnon

Bibliographic record

VenueClinical Pediatrics · 2015
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMontreal Children's Hospital
Fundersnot available
KeywordsFootballMedicineInjury preventionPoison controlOccupational safety and healthAmerican footballSuicide preventionRetrospective cohort studyHuman factors and ergonomicsPhysical therapyFootball playersOddsCohort studyCohortMedical emergencySurgeryLogistic regressionInternal medicineGeography

Abstract

fetched live from OpenAlex

Introduction Little is known about Canadian youth football injuries. The objectives of this study were (a) to contrast the injuries in Canadian and American football players aged 6 to 17 years and (b) compare the injuries sustained during organized football with those in nonorganized football. Methods Using a retrospective cohort design based on data from the Canadian Hospitals Injury Reporting and Prevention Program and the National Electronic Injury Surveillance System a comparison of injuries was made. Results Trends in injuries were comparable. Proportions and odds of injuries were similar, except for a few exceptions. In Canada, more girls were injured and fractures were more prevalent. Compared with nonorganized football, organized football players were older, involved more males, and suffered more traumatic brain injuries and injuries to their lower extremities. Conclusion Canadian and American youth football injuries were similar. The type of football, be it organized or nonorganized, has an impact on injuries.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.067
GPT teacher head0.395
Teacher spread0.328 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations9
Published2015
Admission routes2
Has abstractyes

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