Health Care for NFL Players: <i>Upholding Physician Standards and Enhancing the Doctor‐Patient Relationship</i>
Bibliographic record
Abstract
Beginning my third year with the Kansas City Chiefs and being also a medical student at McGill University, I was at first a little reluctant to comment on Glenn Cohen et al.'s critique of the National Football League's structure involving player health and team doctors, but the opportunity to provide a perspective as both a football player and a medical student was too much to forgo. Because of my athletic and academic background, I am often asked what I think about injuries in professional sports and about the role of sports medicine physicians, and Cohen et al.'s article demands a thoughtful reaction. I want to suggest that the fundamental principles concerning the medical profession and the doctor-patient relationship provide additional arguments for some of the solutions that Cohen et al. discuss. The professional self-regulation that the proposed medical committee could provide and the reliance on a doctor who was not hired by the player's employer-the club-for a second opinion are both good ways to minimize conflicts of interest.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.062 | 0.044 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".