Characterizing PRN Use of Psychotropic Medications for Acute Agitation in Canadian Long-Term Care Residents with Dementia Before and During COVID-19
Bibliographic record
Abstract
Background: Agitation is a disabling neuropsychiatric symptom of dementia. Pro re nata (PRN) injections of psychotropics can be administered for severe acute agitation, but little is known about the frequency of their actual use. Objective: Characterize actual use of injectable PRN psychotropics for severe acute agitation in Canadian long-term care (LTC) residents with dementia and compare use before and during the COVID-19 pandemic. Methods: Residents from two Canadian LTC facilities with orders for PRN haloperidol, olanzapine, or lorazepam between January 1, 2018– May 1, 2019 (i.e., pre-COVID-19) and January 1, 2020– May 1, 2021 (i.e., COVID-19) were identified. Electronic medical records were reviewed to document PRN injections of psychotropic medications and collect data on reason and demographic characteristics. Descriptive statistics were used to characterize frequency, dose, and indications of use, and multivariate regression models were used to compare use between time periods. Results: Of the 250 residents, 45 of 103 (44%) people in the pre-COVID-19 period and 85 of 147 (58%) people in the COVID-19 period with standing orders for PRN psychotropics received ≥1 injections. Haloperidol was the most frequently used agent in both time periods (74% (155/209 injections) pre-COVID-19; 81% (323/398 injections) during COVID-19). Residents in the COVID-19 period were almost two times more likely to receive injections compared with those in the pre-COVID-19 period (odds ratio = 1.96; 95% CI = 1.15–3.34; p = 0.01). Conclusion: Our results suggest that use of PRN injections increased in LTC during the pandemic and contribute to the mounting evidence that agitation worsened during that time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".