Metabolic Monitoring for Patients Treated with Antipsychotic Medications
Bibliographic record
Abstract
OBJECTIVES: Metabolic side effects of antipsychotic treatment include weight gain, dyslipidemia and increased susceptibility to diabetes. Patients with schizophrenia have increased coronary heart disease mortality and reduced life expectancy. There is an urgent clinical need to monitor antipsychotic-treated patients for metabolic disturbance. Our objectives were to review published international monitoring guidelines, establish goals for metabolic monitoring, and make recommendations for practice. METHOD: We reviewed the major published consensus guidelines for metabolic monitoring of patients treated with antipsychotic medications and selectively reviewed practice guidelines for the management of diabetes, dyslipidemia, and hypertension. RESULTS: Patients with serious mental illness have markedly elevated rates of metabolic disturbance and limited access to general medical care. Monitoring, but not necessarily medical treatment of metabolic disorder, falls within the scope of psychiatric practice and should include screening for metabolic disturbance as well as tracking the effects of antipsychotic treatment. In addition, psychiatrists and psychiatric services should work toward facilitating patients' access to medical care. There is considerable consensus in the published guidelines. Areas of dissent include which patients to monitor, the utility of glucose tolerance testing, and the point at which to consider switching antipsychotics. CONCLUSION: We encourage clinicians to adopt a structured system for conducting and recording metabolic monitoring and to develop collaborations with family physicians, diabetes specialists, dieticians, and recreation therapists to facilitate appropriate medical care for antipsychotic-treated patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".