Bibliographic record
Abstract
I want to thank the Journal and the authors for two excellent articles outlining the issues surrounding senior physician competency and the potential problems assessing fitness to practice (JMR Vol. 107 #2). As former chair and delegate for the AMA Senior Physicians Section I was very involved in the AMA’s effort several years ago that looked at the development of guiding principles and possible recommendations for late career physicians. Screening senior physicians would only be possible if cost effective and did not appear punitive, but unfortunately, there is much cultural resistance to externally derived assessment approaches in the United States. Other countries, such as Canada or Australia, have licensing systems that promote universal screening, but with our state-based system, it would be difficult to implement. I agree with Drs. Bundy and Williams that age alone is not sufficient, that cognitive and physical health both need to be taken into consideration. I have long been an advocate of having some sort of attestation at time of license re-registration — but having it provided by the physician’s personal primary care physician, not the individual physician, as self-reporting is not always reliable. This would accomplish two things: establishment of a primary care physician early in a physician’s career, as well as providing some assurance to the public and the licensing board that the physician is fit to practice. I fully realize that implementation of this would probably be difficult for some boards, but it would also eliminate the need for audits.More research is needed, since senior physicians comprise a growing percentage of the U.S. physician workforce and we need to have systems in place that avoid unnecessary reductions in workforce. I look forward to comments from others.
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 | 0.004 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.065 | 0.043 |
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".