Psychotropic medicines are frequently dosed outside recommended ranges: a clinical audit in an Australian mental health hospital
Bibliographic record
Abstract
Abstract Background Compliance with psychotropic dosage guidelines has been shown to improve mental health status, reduce severity of symptoms, and decrease adverse effects. However, guideline recommendations are not always implemented. While deviation from dosage recommendations may be clinically appropriate in some patients, variation can cause a lack of efficacy or patient harm. Aim To evaluate the incidence of antidepressant and antipsychotic prescribing at doses outside the recommended range provided by local guidelines, Therapeutic Guidelines : Psychotropic . Method This study is a retrospective clinical audit of 793 patients admitted to hospital between August 2018 and July 2019. Data were collected through extensive file and chart reviews of patients treated with any of the antidepressant and antipsychotic medications listed in the Psychotropic Dosage Guidelines . Descriptive statistical analyses were performed to determine frequencies and proportions. Results The audit identified that 38.0% of patients received doses of antidepressants or antipsychotics outside the recommended range. Most antidepressants were prescribed within recommended doses (83.0%), with 10.5% above the recommended dose, and 6.2% below. Fewer antipsychotics were prescribed within the recommended range (56.8%), 2.8% were prescribed at doses above the recommended range, and 40.3% were prescribed at doses below the recommendation range. Quetiapine was frequently prescribed at doses lower than recommended. Conclusion The audit revealed a substantial amount of prescribing outside the recommended dosage ranges. It also highlighted the necessity of reviewing policies to limit the use of off‐label, low‐dose quetiapine. Audit and feedback could target psychiatrists who seem to have the highest propensity to prescribe outside the recommended dosage ranges.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".