Bibliographic record
Abstract
THE PHILOSOPHER AND ESSAYIST George Santayana left us with many memorable aphorisms, but the one he is perhaps best known for continues to resonate, year after year. Navigating the world is a kind of dance that requires watching not only where your feet are going next, but where they have just been. The individuals and organizations that dance best take the time to look backward, with purpose, and apply what they see to improve their next steps. As a professional community, we medical regulators should be doing the same — always connecting the past with the future. In this issue, we offer three articles aimed at doing just that. When physicians breech ethical standards, such as committing irregularities on the U.S. Medical Licensing Examination (USMLE), what is the impact on their later professional work? When physicians fail initially on a licensing exam — such as Canada’s MCCQE or the USMLE here in the United States — then successfully take the exam later, do they have poorer medical-practice habits than those who pass the first time? When physicians have a bad case or series of bad cases, how can regulators best determine whether the problem resulted from individual error or from wider system-failures? In all three articles, we see the wisdom of focusing on a physician’s prior behavior and circumstances as we seek to strengthen good habits and prevent future harm to patients. Regulators know through experience that reeducating physicians after a problem or habit becomes pervasive is much more difficult than getting them on the right path after an isolated event. By better connecting past behavior with future accountability in all of our regulatory processes, we are much more likely to keep patients safe.
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 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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".