Metabolic monitoring among patients with psychotic disorders taking antipsychotics: results of a quality improvement project to address this challenging guideline-practice gap
Bibliographic record
Abstract
BACKGROUND: Metabolic adverse effects of antipsychotic medications pose significant health risks for patients with psychotic disorders. Despite clinical practice guidelines recommending regular metabolic monitoring, adherence to these recommendations remains suboptimal in psychiatric settings. OBJECTIVE: This quality improvement project aimed to assess the impact of an organisational paper-based metabolic monitoring form (MMF) on monitoring practices for patients with psychotic disorders receiving antipsychotic medications at an outpatient psychiatric clinic in a large Canadian city. METHODS: A pre-post intervention study was carried out to assess the impact of the MMF on annual monitoring among 75 randomly selected eligible patients. Metabolic monitoring parameters (blood pressure, weight, waist circumference and glucose and lipid profiles) were reviewed 1 year before and after the introduction of the form. RESULTS: The MMF was missing from 10 charts, and despite its presence in the remainder, no improvement was observed in metabolic parameter documentation, and overall guideline adherence remained low. Fasting glucose and HbA1c measurements were most frequently ordered, while blood pressure and weight measurements remained consistently low across both periods. CONCLUSIONS: The implementation of a paper-based MMF alone was insufficient to bridge the guideline-practice gap in metabolic monitoring, highlighting the need for other or concurrent strategies to achieve improvement.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".