Bibliographic record
Abstract
Medical errors and medical cultureThere is no easy way around taking responsibility for mistakesEditor-The case commented on by Singer, Wu, Fazel, and McMillan is chilling in that the patient died in pain and suffering, and in the way it was handled by the senior attending physician-swept under the carpet, information falsified, and given a high minded sort of dismissal with "let this be a lesson." 1 That is almost obscene.The commentaries addressed most of the important points except discussing the fear of litigation and the fact that there are no easy answers when it comes to making mistakes.That needs to be said outright lest someone, especially someone in training who is less experienced, think that admitting a mistake stops at quality control or sharing responsibility, and that there is then some way around the difficult task of actually taking responsibility for the mistake.Within the culture of medicine and even more broadly in modern society there seems to be a drive for finding the easy way out.In this case there is none, and it needs to be made very clear that this is a defining moment in the life of a physician with regard to integrity and professionalism.That must be included in the discussion of how a supervising physician deals with a trainee who has made a mistake, which was relayed with such insight and sensitivity by Wu.
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 | 0.010 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".