Five-Year Trends in Reported National Football League Injuries
Bibliographic record
Abstract
OBJECTIVE: To determine the incidence of all-cause injury and concussion in the National Football League (NFL) over a 5-season time span (2012-2016). DESIGN: Prospective descriptive epidemiological study. SETTING: National Football League Injury Report data from 2012 to 2016. PARTICIPANTS: National Football League players. INTERVENTIONS: None (descriptive study). MAIN OUTCOME MEASURES: Injury report data were collected prospectively for all NFL injuries from 5 seasons (2012-2016). The incidences of reported concussions, knee injuries, and all-cause injury were compared across the 5 seasons using the Kruskal-Wallis rank-sum test. RESULTS: A total of 10 927 injuries were identified across the 5 seasons, including 752 (6.9%) concussions. The top 3 most injured areas included the knee (17.2%), ankle (13.6%), and shoulder (8.8%). Defensive backs consistently had the highest number of all-cause injuries per season. When comparing across years, there was a significant decrease in all-cause injury in 2016 compared with 2015, a significant decrease in knee injuries in 2016 compared with 2015, and a significant increase in concussion in 2015 compared with 2014 (P < 0.05). CONCLUSIONS: Reported all-cause injury incidence and knee injury incidence is currently on the decline. However, reported concussion incidence has recently increased, perhaps due to increased awareness and rule changes implemented to aid in the detection and treatment of concussion. Strategies to reduce injury and improve injury awareness should continue to be explored.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".