Doctors' Duty to Disclose Error: A Deontological or Kantian Ethical Analysis
Bibliographic record
Abstract
Medical (surgical) error is being talked about more openly and besides being the subject of retrospective reviews, is now the subject of prospective research. Disclosure of error has been a difficult issue because of fear of embarrassment for doctors in the eyes of their peers, and fear of punitive action by patients, consisting of medicolegal action and/or complaints to doctors' governing bodies. This paper examines physicians' and surgeons' duty to disclose error, from an ethical standpoint; specifically by applying the moral philosophical theory espoused by Immanuel Kant (ie. deontology). The purpose of this discourse is to apply moral philosophical analysis to a delicate but important issue which will be a matter all physicians and surgeons will have to confront, probably numerous times, in their professional careers.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads 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".