Influence of Extrinsic Risk Factors on National Football League Injury Rates
Bibliographic record
Abstract
BACKGROUND: The risk of injury associated with American football is significant, with recent reports indicating that football has one of the highest rates of all-cause injury, including concussion, of all major sports. There are limited studies examining risk factors for injuries in the National Football League (NFL). PURPOSE: To identify risk factors for NFL concussions and musculoskeletal injuries. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Injury report data were collected prospectively for each week over the 2012-2013 and 2013-2014 regular seasons for all 32 teams. Poisson regression models were used to identify the relationship between predetermined variables and the risk of the 5 most frequent injuries (knee, ankle, hamstring, shoulder, and concussion). RESULTS: A total of 480 games or 960 team games (TGs) from the 2012-2013 and 2013-2014 regular seasons were included in this study. A trend to an increasing risk of concussion and TG ankle injury with decreasing mean game-day temperature was observed. The risk of TG concussion (incidence rate ratio [IRR], 2.16; 95% CI, 1.35-3.45; P = .001) and TG ankle injury (IRR, 1.48; 95% CI, 1.10-1.98; P = .01) was significantly greater for TGs played at a mean game-day temperature of ≤9.7°C (≤49.5°F) compared with a mean game-day temperature of ≥21.0°C (≥69.8°F). The risk of TG shoulder injury was significantly increased for TGs played on grass surfaces (IRR, 1.36; 95% CI, 1.02-1.81; P = .038) compared with synthetic surfaces. The risk of TG injury was not associated with time in season, altitude, time zone change prior to game, or distance traveled to a game. CONCLUSION: This study evaluated extrinsic risk factors for injury in the NFL. A hazardous association was identified for risk of concussion and ankle injury with colder game-day temperature. Further research should be conducted to substantiate this relationship and its potential implication for injury prevention initiatives.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".