Physician characteristics influence the trends in resuscitation decisions at different ages
Bibliographic record
Abstract
AIM: We examined how physicians in different medical specialties would evaluate treatment decisions for vulnerable patients in need of resuscitation. METHODS: A survey depicting six acutely ill patients from newborn infant to aged, all in need of resuscitation with similar prognoses, was distributed (in 2009) to a representative sample of 1650 members of the Norwegian Medical Association and 676 members of the Norwegian Pediatric Association. RESULTS: There were 1335 respondents (57% participation rate). The majority of respondents across all specialties thought resuscitation was in the best interest of a 24 weeks' gestation preterm infant and would resuscitate the patient, but would also accept palliative care on the family's demand. Accepting a family's refusal of resuscitation was more common for the newborn infants. Specialists were overall similar in their answers, but specialty, age and gender were associated with different answers for the patients at both ends of the age spectrum. CONCLUSION: Resuscitation decisions for the very young do not always seem to follow the best interest principle. Specialty and personal characteristics still have an impact on how we consider important ethical issues. We must be cognisant of our own valuations and how they may influence care.
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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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".