Disclosing the Truth: A Dilemma between Instilling Hope and Respecting Patient Autonomy in Everyday Clinical Practice
Bibliographic record
Abstract
BACKGROUND: While medical ethics place a high value on providing truthful information to patients, disclosure practices are far from being the norm in many countries. Transmitting bad news still remains a big problem that health care professionals face in their every day clinical practice. AIMS: Through the review of relevant literature, an attempt to examine the trends in this issue worldwide will be made. METHOD: Various electronic databases were searched by the authors and through systematic selection 51 scientific articles were identified that this literature review is based on. RESULTS: There are many parameters that lead to the concealment of truth. Factors related to doctors, patients and their close environment, still maintain a strong resistance against disclosure of diagnosis and prognosis in terminally ill patients, while cultural influences lead to different approaches in various countries. Withholding the truth is mainly based in the fear of causing despair to patients. However, fostering a spurious hope, hides the danger of its' total loss, while it can disturb patient-doctor relationship.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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; 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".