Physicians' attitudes towards ethical issues and end‐of‐life decision‐making for pediatric patients with unresponsive wakefulness syndrome: An international survey
Bibliographic record
Abstract
AIM: We examined physicians' perspectives on the mental capabilities of pediatric patients with unresponsive wakefulness syndrome (UWS) and their attitudes towards limiting life-sustaining treatment (LST) in an international context. METHOD: A questionnaire survey was conducted among 267 neuropediatricians, practicing in 65 countries. Comparisons were made according to the Human Development Index (HDI) of the countries. The Idler Index of Religiosity was applied to determine religiosity. RESULTS: Participants from countries with a very high HDI were generally more favorable to limiting LST (p < 0.001), specifically cardiopulmonary resuscitation (p = 0.021), intubation/ventilation (p = 0.014), hemodialysis/hemofiltration (p < 0.001), and antibiotic therapy (p < 0.001). Treatment costs that were too high had a weaker influence on their decisions (p < 0.001). Participants who found it never ethically justifiable to limit LST had a higher mean Idler Index of private (p = 0.001) and general (p = 0.020) religiosity and were less satisfied with treatment decisions (p < 0.001) and the communication during the process (p = 0.016). INTERPRETATION: The perspectives towards limiting LST for pediatric patients with UWS are markedly different between physicians from countries with very high and lower HDIs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".