Characterization of Psychotropic PRN Medications in a Canadian Psychiatric Intensive Care Unit
Bibliographic record
Abstract
BACKGROUND: Pro re nata (PRN) antipsychotics and benzodiazepines are routinely used for the rapid stabilization of acutely agitated patients. Despite the popular use of PRN medications in mental health units, primary literature supporting efficacy and safety is poor, and there is no single universally accepted practice guideline. PRN psychotropic medications have the potential to cause adverse effects when used inappropriately. AIMS: Our objective was to characterize the prescribing, administration, and documentation practices of PRN psychotropic medications in a psychiatric intensive care unit. METHODS: We conducted a retrospective chart review of patients admitted to a 12-bed psychiatric intensive care unit between June and September 2018. All PRN antipsychotic and benzodiazepine orders, administrations, documentation practices, and attempted nonpharmacological strategies were assessed for each order and patient. Descriptive statistics were used to analyze data. RESULTS: Thirty-two patients with a total of 123 physicians' orders and 1,179 PRN administrations of antipsychotics and benzodiazepines were reviewed. Of the total administrations, 720 (61%) were combinations with at least two psychotropic agents. Forty-one (33%) physicians' orders had a prescribed indication, and 559 (47%) administrations had an attempted nonpharmacological method prior to PRN administration. Eight patients (25%) had antipsychotic PRN orders, which exceeded the total daily maximum dose. Three adverse drug effects were attributed to PRN administration. CONCLUSIONS: Areas of improvement that we identified included documentation practices of effectiveness of administered PRNs, prescriptions to include clear indications and dosage within the 24-hour maximum limits, and documentation of nonpharmacological methods utilized.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".