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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".