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Record W1939092154 · doi:10.1177/03635465000280041201

The Distribution of Injuries in Men's Canada West University Football: A 5-year Analysis

2000· article· en· W1939092154 on OpenAlexafffundabout
Willem Meeuwisse, Brent Hagel, Nicholas G. Mohtadi, Dale J. Butterwick, Gordon H. Fick

Bibliographic record

VenueThe American Journal of Sports Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of Calgary
FundersUniversity of CalgaryMicrosoft
KeywordsMedicineFootballInjury preventionPhysical therapyConcussionAthletesHamstringPoison controlOccupational safety and healthSports medicineMusculoskeletal injuryCohort studyInjury Severity ScoreEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

We conducted a prospective cohort study from 1993 to 1997 to determine the frequency and severity of injury in men's Canada West university football. The Canadian Intercollegiate Sport Injury Registry was used to document baseline preseason data, daily athlete participation, and subsequent injury from five varsity football teams. An injury was defined as "any injury resulting in one or more complete or partial sessions of time loss" or "any concussion or transient neck neurologic injury." The annual proportion of injured athletes ranged from 53.5% to 60.4%, with a 5-year total of 1,811 injuries. Regression analysis indicated that the rate of nonconcussion, nonneck neurologic injuries increased. Concussion (N = 110), hamstring strain (N = 88), and brachial plexus (N = 84) injuries were the most common, specific injury diagnoses. Knee injuries resulted in the highest rate of severe (greater than or equal to 7 sessions of time loss) injury and resulted in the most time loss (3,350.5 sessions). Ligament sprains and muscle strains and spasms accounted for approximately half of all injury diagnoses. A total of 1,173 injuries (65%) were related to contact between players or between players and other obstacles. Future studies should be conducted to identify risk factors for the ultimate purpose of implementing injury prevention strategies.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.323
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.003
GPT teacher head0.224
Teacher spread0.221 · 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 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

Citations58
Published2000
Admission routes3
Has abstractyes

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