Assessing cardiometabolic parameter monitoring in inpatients taking a second-generation antipsychotic: The CAMI-SGA study – a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This study aims to determine the proportion of initial cardiometabolic assessment and its predicting factors in adults with schizophrenia, bipolar disorder or other related diagnoses for whom a second-generation antipsychotic was prescribed in the hospital setting. DESIGN: Cross-sectional study. SETTING: The psychiatry unit of a Canadian tertiary care teaching hospital in Montreal, Canada. PARTICIPANTS: 402 patients with aforementioned disorders who initiated, restarted or switched to one of the following antipsychotics: clozapine, olanzapine, risperidone, paliperidone or quetiapine, between 2013 and 2016. PRIMARY OUTCOME MEASURES: We assessed the proportion of cardiometabolic parameters monitored. SECONDARY OUTCOME MEASURES: We identified predictors that influence the monitoring of cardiometabolic parameters and we assessed the proportion of adequate interventions following the screening of uncontrolled blood pressure and fasting glucose or glycated haemoglobin (HbA1c) results. RESULTS: Only 37.3% of patients received monitoring for at least three cardiometabolic parameters. Blood pressure was assessed in 99.8% of patients; lipid profile in 24.4%; fasting glucose or HbA1c in 33.3% and weight or body mass index in 97.8% of patients while waist circumference was assessed in 4.5% of patients. For patients with abnormal blood pressure and glycaemic values, 42.3% and 41.2% subsequent interventions were done, respectively. The study highlighted the psychiatric diagnosis (substance induced disorder OR 0.06 95% CI 0.00 to 0.44), the presence of a court-ordered treatment (OR 0.79 95% CI 0.35 to 1.79) and the treating psychiatrist (up to OR 34.0 95% CI 16.2 to 140.7) as predictors of cardiometabolic monitoring. CONCLUSIONS: This study reports suboptimal baseline cardiometabolic monitoring of patients taking an antipsychotic in a Canadian hospital. Optimising collaboration within a multidisciplinary team may increase cardiometabolic monitoring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".