Bibliographic record
Abstract
PURPOSE: The objective of this cohort study was to determine the level of off-season sport specific activity, peak isometric adductor torque, and hip abduction flexibility that are predictive of groin or abdominal strain injury in the National Hockey League (NHL). METHODS: The subjects were 1292 consenting NHL players. Estimated relative risks of injury are reported using the following exposures: 1) level of sport specific training in the off-season, 2) peak isometric adductor torque, 3) total hip abduction flexibility, 4) previous injury, 5) years of NHL experience, and 6) skate blade hollow measurement. Estimates of probability of injury are predicted for various levels of exposures on the basis of logistic regression analysis. RESULTS: During training camp, players who reported less than 18 sessions sport specific training in the off-season were at greater than three times the risk of injury than those who did not (relative risk (RR); 3.38 95% confidence interval (CI), 1.45-7.92). Players who reported previous history of this injury were at more than two times the risk of injury than those who did not (RR, 2.88; 95% CI, 1.33-6.26). Veterans were at greater than five times the risk of injury than rookies (RR, 5.69; 95% CI, 2.05-15.85). Peak isometric adductor torque, total abduction flexibility, and skate blade hollow measurement were not predictive of injury. There is evidence of a dose-response gradient as predicted probability of injury decreases with increasing levels of sport specific training. In the regular season, sport specific training was not as strong a risk factor (RR, 2.32; 95% CI, 1.0-5.39). CONCLUSION: Low levels of off-season sport specific training and previous injury are clearly risks for groin injury at an elite level of hockey. Future research is required to investigate prevention strategies for this injury in hockey.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".