Monitoring and Management of Metabolic Risk Factors in Outpatients Taking Antipsychotic Drugs: A Controlled Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the screening, monitoring, and management of metabolic risk factors and diseases in long-term antipsychotic users in relation to current practice guidelines and current standards of care as represented by a control group from an HIV clinic. METHODS: We undertook a retrospective chart review of mental health clinic outpatients taking antipsychotic drugs long-term (cases) and HIV outpatients prescribed highly active antiretroviral therapy (control subjects). RESULTS: We included 99 mental health clinic patients and 98 HIV patients in the analysis. According to information available in the outpatient clinic chart, the 10-year coronary artery disease risk was computable for 28% of the mental health clinic patients (mean risk 11.9%) and for 90% of the HIV patients (mean risk 9.5%) (chi2 = 77.0, P < 0.001). Metabolic risk factors were less frequently documented in mental health clinic charts. All HIV clinic patients were screened for hypertension and diabetes, and 90% were screened for dyslipidemia, whereas this information was missing for 30%, 39%, and 60% of mental health clinic patients, respectively (P < 0.001 for all). Disease monitoring was also more comprehensive in HIV clinic charts (for example, 100% of HIV patients were monitored for lipids, compared with 71% of mental health clinic patients; P = 0.001). CONCLUSIONS: Improved efforts are needed in the somatic care of patients with bipolar disorder and schizophrenia who are taking antipsychotics, given that they typically have moderate-to-high risk for metabolic diseases.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".