Prevention of concussion and long‐term effects of repetitive traumatic brain injury: Unanimous consensus from the Cantu Concussion Summit
Bibliographic record
Abstract
OBJECTIVE: The Cantu Concussion Summit aimed to gather clinicians and researchers to share findings and identify research gaps in brain injury and long-term cognitive disorders in athletes. DESIGN: The conference concluded with a discussion of ways to best mitigate the risk of concussion and repetitive traumatic brain injury (RTBI). SETTING AND PARTICIPANTS: The summit was supported by an unrestricted educational grant from the National Football League and featured a diverse group of experts from multiple disciplines. INTERVENTIONS: N/A. MAIN OUTCOME MEASURES: This discussion resulted in unanimous agreement supporting six consensus statements aimed at enhancing player safety and health. RESULTS: These consensus statements are as follows: (1) Eliminate intentional and avoidable head impacts in sports practices and games. (2) Encourage policies that limit the number, duration, and intensity of head impacts during sports practices and games. (3) Reinforce proper and safer techniques to avoid head contact at all levels of play. (4) Implement rules that reduce and penalize intentional and avoidable contact to the head and neck. (5) Investigate specific clinical signs and symptoms associated with chronic traumatic encephalopathy neuropathology through further research. (6) Improve the criteria for traumatic encephalopathy syndrome through continued research. CONCLUSIONS: These consensus statements highlight opportunities to advance the understanding and prevention of concussions and RTBI, by emphasizing the need for ongoing research and policy changes to safeguard athletes' health and well-being. Implementation of these changes would reduce the burden of brain injuries in sports, promoting a safer environment for athletes at all levels.
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 | 0.148 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 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".