© 2005 CMA Media Inc. or its licensors Letters
Bibliographic record
Abstract
Physician, regulate thyself! We could not agree more with aCMAJ editorial1 that suggests confidence in physicians is at the core of what we do. We also agree that strong licensing and regulatory bodies are needed. Long before the Shipman case came to light in the United Kingdom, med-ical regulatory authorities in Canada began making significant progress to-ward transparency and increased public accountability. There are now more public representatives on the councils of the regulatory authorities, and most disciplinary hearings are open to the public and the media. Furthermore, the medical regula-tory authorities recognize that a physi-cian’s performance may decline over time, and that the quality and safety of any individual physician’s practice need regular review. Thus, the top priority for the Federation of Medical Regula-tory Authorities of Canada is revalida-tion of licensure. Medical regulatory authorities around the world are examining the recommendations in the fifth report of the Shipman Inquiry2 with a view to doing everything possible to prevent a similar occurrence in their own juris-dictions. Although our organizations must learn from this sad and appalling case, it is an extreme example of failure in a multicomponent system and should not be viewed as representative of the system as a whole.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.714 | 0.524 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".