Shame and Secrecy of Do Not Resuscitate Orders: An Historical Review and Suggestions for the Future
Bibliographic record
Abstract
This paper clarifies some of the longstanding difficulties in negotiating Do Not Resuscitate Orders by reframing the source of the dilemmas as not residing with either the patient or the physician but with their relationship. The recommendations are low cost and low-tech ways of making major improvements to the care and quality of life of the most ill patients in hospital. With impending physician-assisted death legislation there is an urgency to find more efficient and beneficial ways for clinicians and patients to address resuscitation issues at the bedside. Paradigmatic shifts in the nature of the patient-physician relationship will need to be encouraged by the larger community. These encouraged shifts address the concepts of passive/inferior patient – active/superior physician, patient ownership of and access to all their health care information, and treating the patient as a major participant in the delivery of health care. These recommended changes will not in themselves make any patient, physician or other healthcare provider more humane and open in the patient’s final days. The goal, instead, is to have changes to the context of the discussion provide an encouraging environment for more open communication and a balanced relationship among participants with the patient being the most important.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".