Analysis and Evolution of Head Injury in Football
Bibliographic record
Abstract
OBJECTIVE: To review head injury in football through historical, anatomic, and physiological analysis. METHODS: We obtained data from a thorough review of the literature. RESULTS: The reported incidence of concussion among high school football players dropped from 19% in 1983 to 4% in 1999. During the 1997 Canadian Football League season, players with a previous loss of consciousness in football were 6.15 times more likely to experience a concussion than players without a previous loss of consciousness (P < 0.05). Players with a previous concussion in football were 5.10 times more likely to experience a concussion than players without a previous concussion (P = 0.0001). With the implementation of National Operating Committee on Standards for Athletic Equipment standards, fatalities decreased by 74% and serious head injuries decreased from 4.25 per 100,000 to 0.68 per 100,000. CONCLUSION: Significant declines in both the incidence and severity of head injury have been observed. The enhanced safety records in football can be attributed to the application of more stringent tackling regulations as well as the evolving football helmet. The role of a neurosurgeon is critical in further head injury prevention and guidelines in sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".