ORIGINAL INVESTIGATION Choosing Your Words Carefully How Physicians Would Disclose Harmful Medical Errors to Patients
Bibliographic record
Abstract
Background: A gap exists between patients ’ desire to be told about medical errors and present practice. Little is known about how physicians approach disclosure. The objective of the present study was to describe how phy-sicians disclose errors to patients. Methods:Mailed surveyof 2637medical and surgical phy-sicians in theUnited States (Missouri andWashington) and Canada (national sample). Participants received 1 of 4 sce-nariosdepictingseriouserrors thatvariedbyspecialty (medi-cal and surgical scenarios) and by how obvious the error would be to the patient if not disclosed (more apparent vs less apparent). Five questions measured what respon-dents would disclose using scripted statements. Results: Wide variation existed regarding what informa-tion respondentswould disclose.Of the respondents, 56%
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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 | 0.015 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.003 |
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".