Pharmacological Management of Agitation and Delirium in Older Adults: a Survey of Practices in Canadian Emergency Departments
Bibliographic record
Abstract
Agitation is a common presenting symptom of delirium for older adults in the emergency department (ED). No medications have been found to reduce delirium severity, symptoms, or mortality, yet they may cause harm. Guidelines suggest using medications only when patients are posing a risk of harm, situations which may arise frequently in the ED. We sought to characterize prescribing patterns of medications for agitation by ED physicians in Canadian hospitals. In this multicenter study, we surveyed physicians in Vancouver, Toronto, and Sherbrooke. Descriptive statistics were used to summarize group characteristics and starting doses were compared to order sets. Fisher exact tests were used for demographic comparison. Ordinal linear regression models were run to identify a relationship between starting dose of medications and location. Of the 137 physicians invited, 77 (56%) completed the survey. Use of order sets was greatest in Sherbrooke and least in Vancouver. The most common medications used across sites were haloperidol, lorazepam, and quetiapine. Benzodiazepines were used across all sites but were used significantly more frequently in Vancouver than the other sites. Practice location was a significant predictor of starting dose of haloperidol, with Sherbrooke and Toronto having a lower starting dose than Vancouver. Higher use of order sets correlated with lower and more consistent starting doses. Benzodiazepines are used across EDs in Canada despite little evidence for efficacy in delirium and risk of harm. Implementation of order sets may be a useful way to standardize ED management of older adults experiencing hyperactive 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".