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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".