Descriptive Epidemiology of Musculoskeletal Injuries and Concussions in the National Football League, 2012-2014
Bibliographic record
Abstract
BACKGROUND: The risk of all-cause injury and concussion associated with football is significant. The National Football League (NFL) has implemented changes to increase player safety warranting investigation into the incidence and patterns of injury. PURPOSE: To document the incidence and patterns of all-cause injury and concussions in the NFL. STUDY DESIGN: Descriptive epidemiology study. METHODS: Injury data were collected prospectively from official NFL injury reports over 2 regular seasons from 2012 to 2014, with identification of injury incidence rates and patterns. Concussion rate ratios were calculated using previously reported NFL rates. RESULTS: A total of 4284 injuries were identified, including 301 concussions. The all-cause injury rate was 395.8 per 1000 athletes at risk (AAR) and concussion incidence was 27.8 per 1000 AAR. Only 2.3% of team games were injury free. Wide receivers, tight ends, and defensive backs had the highest incidence of injury and concussion. Concussion incidence was 1.61-fold higher in 2012 to 2014 compared with 2002 to 2007. The knee was injured most frequently, followed by the ankle, hamstring, shoulder, and head. CONCLUSION: The incidence of all-cause injury and concussion in the NFL is significant. Concussion injury rates are higher than previous reports, potentially reflecting an improvement in recognition and awareness. Injury prevention efforts should continue to reduce the prevalence of injury associated with football.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".