Expert opinion on detecting and treating depression in palliative care: A Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is a dearth of data regarding the optimal method of detecting and treating depression in palliative care. This study applied the Delphi method to evaluate expert opinion on choice of screening tool, choice of antidepressant and choice of psychological therapy. The aim was to inform the development of best practice recommendations for the European Palliative Care Research Collaborative clinical practice guideline on managing depression in palliative care. METHODS: 18 members of an international, multi-professional expert group completed a structured questionnaire in two rounds, rating their agreement with proposed items on a scale from 0-10 and annotating with additional comments. The median and range were calculated to give a statistical average of the experts' ratings. RESULTS: There was contention regarding the benefits of screening, with 'routine informal asking' (median 8.5 (0-10)) rated more highly than formal screening tools such as the Hospital Anxiety and Depression Scale (median 7.0 (1-10). Mirtazapine (median 9 (7-10) and citalopram (median 9 (5-10) were the considered the best choice of antidepressant and cognitive behavioural therapy (median 9.0 (3-10) the best choice of psychological therapy. CONCLUSIONS: The range of expert ratings was broad, indicating discordance in the views of experts. Direct comparative data from randomised controlled trials are needed to strengthen the evidence-base and achieve clarity on how best to detect and treat depression in this setting.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it