Routine advance directive and organ donation questioning on admission to hospital.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to determine whether routine questioning on admission to hospital can identify patients who do not want cardiopulmonary resuscitation (CPR) or intensive-care unit (ICU) admission. We also determined whether routine questioning can identify patients interested in organ donation. Finally, we determined whether routine questioning helps train housestaff to discuss end-of-life issues with patients. METHODS: The housestaff of an internal-medicine ward questioned admitted patients about previous discussions regarding resuscitation or organ donation, and about preferences regarding CPR, ICU, and organ donation. RESULTS: Of 40 patients who had never discussed CPR and ICU issues, 25 per cent preferred no CPR, and 18 per cent preferred no ICU. Of 24 patients who had never discussed organ-donation issues, 67 per cent were interested in being donors. All housestaff felt more comfortable discussing end-of-life issues by the end of the study. CONCLUSION: Routine questioning of patients on admission identifies a significant number who prefer no CPR or ICU. This information may help to avoid inappropriate resuscitation efforts and ICU admissions. Routine questioning also reveals many patients who are interested in organ donation. Encouraging these patients to identify themselves and to discuss their wishes with family members may increase the number of procurable organs. Housestaff benefit from the experience of discussing end-of-life issues.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".