Metabolic and Endocrine Disturbances in Psychiatric Disorders: A Multidisciplinary Approach to Appropriate Atypical Antipsychotic Utilization
Bibliographic record
Abstract
Patients with psychiatric disorders have an increased rate of cardiovascular morbidity and mortality compared with the general population. Metabolic issues such as weight gain, dyslipidemia, diabetes mellitus, diabetic ketoacidosis,and pancreatitis have been reported with the use of antipsychotic agents. Although atypical antipsychotics have not been linked directly to the development of metabolic syndrome, these medications have been shown to increase risk factors that can lead to metabolic and endocrine disturbances. Therefore, clinicians should provide ongoing monitoring for patients who are being treated for psychiatric disorders with these agents. According to the 2004 Consensus Report on Antipsychotics, screening measures should include baseline and follow-up monitoring of personal/family histories, weight (body mass index), waist circumference, blood pressure, fasting plasma glucose, and fasting lipid profile.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".