Ethics, Choices, and Decisions in Acute Medicine
Bibliographic record
Abstract
OBJECTIVE: To study the attitudes of Norwegian physicians to resuscitation of hypothetical patients all at risk of neurological sequelae. DESIGN: Mail-based survey. SETTING: A cohort of Norwegian physicians who are representative of the national physician corps. INTERVENTIONS: A total of 1650 Norwegian physicians (7% of practicing physicians in Norway) received a written questionnaire describing six scenarios of patients all in need of emergency life-saving intervention. Respondents were asked whether they would resuscitate; whether such resuscitation was in the patient's best interest; whether a surrogate's refusal of intervention would be accepted; and whether they would have wanted resuscitation if the patient were their own child, their spouse, or themselves. Positive or negative responses on a four-point Likert scale were recorded. MEASUREMENTS AND MAIN RESULTS: A total of 1,069 respondents (response rate, 65%). Physicians responding to these scenarios were a) more inclined to resuscitate an anonymous patient than if the patient were themselves or their kin; b) willing to resuscitate although they do not consider this intervention to be in the patient's best interest; c) willing to refrain from resuscitation on surrogate request in spite of a reasonably good prognosis; d) willing to accept surrogate's refusal of resuscitation in spite of a stated opinion that such intervention would be in the patient's best interest; and e) less willing to resuscitate newborn infants compared with older children and adults (except the aged) with similar prognoses. CONCLUSION: There appear to be differences in medical thinking about best interest, surrogate decision making, and the relative value of lives as far as these are applied to acute, life-saving treatment.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".