Psychoactive Medications and Risk of Delirium in Hospitalized Cancer Patients
Bibliographic record
Abstract
PURPOSE: Psychoactive medications are biologically plausible and potentially modifiable risk factors of delirium. To date, however, research findings are inconsistent regarding their association with delirium. The association between exposure to anticholinergics, benzodiazepines, corticosteroids, and opioids and the risk of delirium was studied. PATIENTS AND METHODS: A total of 261 hospitalized cancer patients were followed up with repeated assessments by using the Nursing Delirium Screening Scale for up to 4 weeks for incident delirium. Detailed exposure to psychoactive medications was documented daily. Strengths of association with delirium were expressed as hazard ratios (HRs) in univariate and multivariate analyses by using Cox regression models. All medication variables were coded as time-dependent covariates. Whenever possible, exposure was computed by using cumulative daily doses in equivalents; dichotomous cutoffs were determined. RESULTS: During follow-up (mean, 8.6 days), 43 patients became delirious (16.5%). Delirium was associated with a history of delirium and the presence of hepatic metastases at admission. Analysis of the effect of medications was performed adjusting for these factors. Patients exposed to daily doses of benzodiazepines above 2 mg (HR, 2.04; 95% CI, 1.05 to 3.97), above 15 mg of corticosteroids (HR, 2.67; 95% CI, 1.18 to 6.03), or above 90 mg of opioids (HR, 2.12; 95% CI, 1.09 to 4.13) had increases in the risks for delirium. We did not observe associations between anticholinergics and risk for delirium. CONCLUSION: Exposure to opioids, corticosteroids, and benzodiazepines is independently associated with an increased risk of delirium in hospitalized 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".