Exploring end-of-life decision-making in China for disorders of consciousness
Bibliographic record
Abstract
Objectives We aim to investigate the ethical attitudes of the Chinese population toward withdrawal of life-sustaining treatment (WLST) in disorders of consciousness (DoC) patients.Methods A self-administered questionnaire concerning WLST was distributed to Chinese medical professionals and non-medical participants between February and July 2022. Statistical analysis included chi-square tests and logistic regressions.Results A total of 1223 Chinese participants responded to the questionnaire (39% of whom were medical professionals). Less than one third of participants reported positive attitudes towards withdrawing artificial nutrition and hydration (ANH), antibiotics, and do-not-resuscitation (DNR) orders in patients with unresponsive wakefulness syndrome (UWS) (30%, 24%, 24%) and minimally conscious state (MCS) (23%, 19%, 15%). More respondents agreed with WLST in UWS compared to MCS (p < 0.05). Positive attitudes toward DNR orders were associated with participants’ older age, religion, monthly income > 5000 RMB and medical profession (p < 0.05). Most participants deemed patient’s will (78%), families’ wishes (67%), and financial burden (63%) to be crucial factors when considering WLST.Conclusions Chinese respondents exhibit a relatively low propensity to accept WLST in DoC. Ethical attitudes toward WLST resulted to be affected by individual characteristics of responders. These results call for developing better regulations for identifying qualified surrogate decision-makers and reducing legal ambiguities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".