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Record W2113253049 · doi:10.1177/1941738114540445

Ability of Preseason Body Composition and Physical Fitness to Predict the Risk of Injury in Male Collegiate Hockey Players

2014· article· en· W2113253049 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSports Health A Multidisciplinary Approach · 2014
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsIce hockeyAthletesPhysical therapyMedicineInjury preventionLogistic regressionPoison controlPhysical medicine and rehabilitationEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Injuries in collegiate ice hockey can result in significant time lost from play. The identification of modifiable risk factors relating to a player's physical fitness allows the development of focused training and injury prevention programs targeted at reducing these risks. PURPOSE: To determine the ability of preseason fitness outcomes to predict in-season on-ice injury in male collegiate ice hockey players. STUDY DESIGN: Prognostic cohort study. LEVEL OF EVIDENCE: Level 3. METHODS: Athlete demographics, percentage body fat, aerobic capacity (300-m shuttle run; 1-, 1.5-, 5-mile run), and strength assessment (sit-ups, push-ups, grip strength, bench press, Olympic cleans, squats) data were collected at the beginning of 8 successive seasons for 1 male collegiate ice hockey team. Hockey-related injury data and player-level practice/game athlete exposure (AE) data were also prospectively collected. Seventy-nine players participated (203 player-years). Injury was defined as any event that resulted in the athlete being unable to participate in 1 or more practices or games following the event. Multivariable logistic regression was performed to determine the ability of the independent variables to predict the occurrence of on-ice injury. RESULTS: There were 132 injuries (mean, 16.5 per year) in 55 athletes. The overall injury rate was 4.4 injuries per 1000 AEs. Forwards suffered 68% of the injuries. Seventy percent of injuries occurred during games with equal distribution between the 3 periods. The mean number of days lost due to injury was 7.8 ± 13.8 (range, 1-127 days). The most common mechanism of injury was contact with another player (54%). The odds of injury in a forward was 1.9 times (95% CI, 1.1-3.4) that of a defenseman and 3 times (95% CI, 1.2-7.7) that of a goalie. The odds of injury if the player's body mass index (BMI) was ≥25 kg/m(2) was 2.1 times (95% CI, 1.1-3.8) that of a player with a BMI <25 kg/m(2). The odds ratios for bench press, maximum sit-ups, and Olympic cleans were statistically significant but close to 1.0, and therefore the clinical relevance is unknown. CONCLUSION: Forwards have higher odds of injury relative to other player positions. BMI was predictive of on-ice injury. Aerobic fitness and maximum strength outcomes were not strongly predictive of on-ice injury.

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.

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)0.002
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 score0.031
Threshold uncertainty score0.518

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.311
Teacher spread0.300 · 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