Delirium prevention in terminal cancer: assessment of a multicomponent intervention
Bibliographic record
Abstract
OBJECTIVE: Delirium is a highly prevalent and deleterious disorder in terminally ill cancer patients. We assessed whether a multicomponent preventive intervention was effective in decreasing delirium incidence and severity among cancer patients receiving end-of-life care. METHODS: A cohort of 1516 patients was followed from admission to death at seven Canadian palliative care centers. In two of these centers, routine care included a delirium preventive intervention targeting physicians (written notice on selective delirium risk factors and inquest on intended medication changes), patients, and their family (orientation to time and place, information about early delirium symptoms). Delirium frequency and severity were compared between patients at the intervention (N = 674) and usual-care (N = 842) centers based on thrice-daily symptom assessments with the Confusion Rating Scale. RESULTS: The overall rate of adherence to the intervention was 89.7%. The incidence of delirium was 49.1% in the intervention group, compared with 43.9% in the usual-care group (odds ratio [OR] 1.23, P = 0.045). When confounding variables were controlled for, no difference was observed between the intervention and the usual-care groups in delirium incidence (OR 0.94, P = 0.66), delirium severity (1.83 vs. 1.92; P = 0.07), total days in delirium (4.57 vs. 3.57 days; P = 0.63), or duration of first delirium episode (2.9 vs. 2.1 days; P = 0.96). Delirium-free survival was similar in the two groups. CONCLUSION: A simple multicomponent preventive intervention was ineffective in reducing delirium incidence or severity among cancer patients receiving end-of-life care. Delirium prevention remains a difficult challenge in terminally ill cancer patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".