Physical Health and Drug Safety in Individuals with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: While antipsychotic medications are the mainstay of therapy for individuals with schizophrenia and psychotic disorders, their use is associated with adverse effects on physical health that require the attention and care of prescribers. METHODS: We used the ADAPTE process to adapt existing guideline recommendations from the National Institute for Health and Care Excellence (NICE) and Scottish Intercollegiate Guidelines Network (SIGN) guidelines on the dosing of antipsychotics and antipsychotic polypharmacy, screening for adverse effects of antipsychotics, and management of metabolic and extrapyramidal side effects to the Canadian context. RESULTS: Prescribers are encouraged to use the lowest effective dose and to avoid the routine use of multiple antipsychotics. Scheduled monitoring of body mass index, waist circumference, blood pressure, glucose, lipids, prolactin, electrocardiograms, and extrapyramidal symptoms is recommended. Lifestyle interventions are recommended to mitigate antipsychotic-induced weight gain. Prescribers should follow Canadian guidelines on the treatment of obesity, dyslipidemia, and diabetes. Recommendations on antipsychotic drug choice are made for users particularly concerned about extrapyramidal symptoms. CONCLUSION: Careful monitoring and attention by prescribers may mitigate adverse effects associated with antipsychotic medications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".