Opioid medications and longitudinal risk of delirium in hospitalized cancer patients
Bibliographic record
Abstract
BACKGROUND: Delirium is an important problem in hospitalized cancer patients. The objective of this study was to determine whether exposure to corticosteroids, benzodiazepines, or opioids predicted delirium. METHODS: A prospective cohort study was conducted in an oncology/internal medicine population. Patients were assessed continuously for the presence of delirium until they were discharged by using the Nursing Delirium Screening Scale (Nu-DESC). Follow-up for outcome began after incident delirium. The primary outcome was the presence of a delirium event, which was defined as a Nu-DESC score >1. Strengths of associations of medications with delirium were expressed as odds ratios (ORs) in univariate and multivariate analyses. RESULTS: In total, 114 patients (1823 patient-days) met the inclusion criteria for the study. The mean follow-up from incident delirium was 16 days. The mean number of delirium events by patient was 6 (total number, 667 delirium events). Analysis by day on several occasions revealed significant associations between opioids and delirium. Corticosteroids and benzodiazepines were not associated significantly with an increased risk of delirium on any given day. Analysis by patient using generalized estimating equation (GEE) models showed an increased risk of delirium on any day of follow-up associated with opioid exposure in univariate analysis (OR of 1.70; P<.0001). The association remained significant after adjustment for corticosteroid, benzodiazepine, and antipsychotic exposure using GEE regressions (OR of 1.37; P=.0033). Truncating follow-up at 30 days did not affect the results (OR of 1.38; P<.032). CONCLUSIONS: Exposure to opioids during hospitalization was associated significantly with an increased longitudinal risk of delirium.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".