Delirium management by palliative medicine specialists: a survey from the association for palliative medicine of Great Britain and Ireland
Bibliographic record
Abstract
OBJECTIVES: Delirium is common in palliative care settings. Management includes detection, treatment of cause(s), non-pharmacological interventions and family support; strategies which are supported with varying levels of evidence. Emerging evidence suggests that antipsychotic use should be minimised in managing mild to moderate severity delirium, but the integration of this evidence into clinical practice is unknown. METHODS: A 21-question online anonymous survey was emailed to Association for Palliative Medicine members in current clinical practice (n=859), asking about delirium assessment, management and research priorities. RESULTS: Response rate was 39%: 70% of respondents were palliative medicine consultants. Delirium guidelines were used by some: 42% used local guidelines but 38% used none. On inpatient admission, 59% never use a delirium screening tool. Respondents would use non-pharmacological interventions to manage delirium, either alone (39%) or with an antipsychotic (58%). Most respondents (91%) would prescribe an antipsychotic and 6% a benzodiazepine, for distressing hallucinations unresponsive to non-pharmacological measures. Inpatient (57%) and community teams (60%) do not formally support family carers. Research priorities were delirium prevention, management and prediction of reversibility. CONCLUSION: This survey of UK and Irish Palliative Medicine specialists shows that delirium screening at inpatient admission is suboptimal. Most specialists continue to use antipsychotics in combination with non-pharmacological interventions to manage delirium. More support for family carers should be routinely provided by clinical teams. Further rigorously designed clinical trials are urgently needed in view of management variability, emerging evidence and perceived priorities for research.
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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 | 0.009 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".