Clinical and ethical challenges in decision-making for patients with disorders of consciousness and locked-in syndrome from Chinese neurologists’ perspectives
Bibliographic record
Abstract
Background: The diagnosis of and life-sustaining treatment (LST) for patients with disorders of consciousness (DoC) and locked-in syndrome (LIS) have been the subject of intense debate. Objective: We aim to investigate the application of diagnostic knowledge, opinions about the administration of LST, and ethical challenges related to DoC and LIS. Design: A cross-sectional study. Methods: A survey was conducted among Chinese neurologists. Questionnaires included three vignettes (unresponsive wakefulness syndrome (UWS); minimally conscious state (MCS), and LIS). They were randomly distributed among neurologists from August 2018 to December 2019. Results: A sample of 360 questionnaires was included (response rate: 78%). Overall, 63% of the participants chose the correct diagnostic category. The neurologists who received the MCS case chose the category more accurately than the neurologists with the UWS ( p < 0.001) and LIS case ( p = 0.002). Most neurologists preferred never to limit LST for their patients (47%, 63%, and 67% in UWS, MCS, and LIS groups, p = 0.052). A large group of neurologists believed UWS patients could feel pain (73%), with no difference from MCS and LIS patients ( p > 0.05). Deciding for patients in the absence of surrogates was rated extremely challenging. Conclusion: A large proportion of Chinese neurologists in our study didn’t apply the accurate diagnostic categories to the description of DoC and LIS patients. This calls for more education and training. Most Chinese neurologists were reluctant to limit LST for patients. This may indicate that there may be a need to emphasize the allocation of more resources toward long-term care in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".