Bibliographic record
Abstract
GENERAL COLIN POWELL’S ADVICE about preparation and learning from failure has strong implications for the medical regulatory community, which relies on a combination of forward-thinking and backward-reflecting as it constantly seeks to adapt and evolve in order to protect the public in a changing environment for medicine. When General Powell died recently of complications from COVID-19, he left behind a legacy of forthrightness, transparency and learning from his mistakes. We medical regulators would do well to keep his words in mind as we seek to achieve the delicate balance between sanctioning physicians and, at the same time, making it possible for them to go on with their careers — when appropriate — following such sanctions. This requires carefully evaluating the circumstances that lead to regulatory violations or other issues that regulators must contend with — and then doing our best to prevent them from happening again. Physicians, too, must follow this model, as state boards display transparency about the sanctions imposed against them and make efforts to allow them to continue to practice — under the strong expectation that they will have learned from their mistakes and the process of being disciplined. In this issue of JMR we feature three articles with relevance to learning from mistakes of the past while preparing for the future. In “Protecting Patients from Egregious Wrongdoing by Physicians: Consensus Recommendations from State Medical Board Members and Staff” (page 5) we are offered a list of strategies that state medical boards could employ now to strengthen current patient protections going forward — gleaned from interviews with U.S. regulatory leaders. In “Exploring Health Professional Criminality and Competence Using the Case of Canadian Health Care Serial Killer Elizabeth Wettlaufer” (page 19) we learn the disturbing details of an infamous killing spree in long-term care homes and how failures of communication between regulators, health care employers and others contributed to a tragic — but avoidable — outcome. And in “Expecting the Unexpected: How Regulators Can Prepare for Serious Events” (page 28) leaders from the International Association of Medical Regulatory Authorities outline steps that medical regulators should be considering as they find themselves increasingly dealing with pandemics, climate crises and other formidable threats.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.316 | 0.195 |
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".