Tragic Knowledge: Truth Telling and the Maintenance of Hope in Surgery
Bibliographic record
Abstract
Surgeons often are faced with the challenge of balancing truth telling and the maintenance of hope in the setting of a poor prognosis. This ethical dilemma is informed by conflicting appeals to principles of autonomy and nonmaleficence, where a patient's right to be told important medical information must be weighed against the potential harm that may result from the knowledge of an unfavourable diagnosis. Truth telling in surgery raises questions on the nature of truth itself, how much information ought to be shared, what information can be withheld, and how surgeons should share tragic knowledge with patients. This paper will address these questions and provide some insight on how surgeons may navigate the sharing of tragic knowledge.
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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 | 0.026 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.069 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".