Safety and tolerability of atypical antipsychotics in patients with bipolar disorder: prevalence, monitoring and management
Bibliographic record
Abstract
Atypical antipsychotics are associated with fewer movement disorders and a lower risk of tardive dyskinesia than conventional antipsychotics, but are not without side-effects. Metabolic side-effects associated with some of the atypical antipsychotics are a concern for both clinicians and patients. Adverse events related to central nervous system effects, weight gain, and alterations in glucose, lipid, and prolactin levels in patients with depression, bipolar, and anxiety disorders have been reported. Balancing the significant benefits of treatment with these agents against the potential risks of metabolic disturbances and other adverse effects is crucial. Emerging data are making it possible to determine the risk-benefit analysis for specific atypical antipsychotics in individual patients and allow for targeted selection of treatment. A new concept of effectiveness is emerging that attempts to balance adverse effects of treatment with patient quality of life. Patients treated with atypical antipsychotics should have their weight, waist circumference, glucose, and lipids monitored on a regular basis. Monitoring of prolactin levels is not suggested; however, a baseline measurement before initiating treatment can be useful, with subsequent assessment only if a patient demonstrates symptoms. Prevention of weight gain is important. Diet and exercise should be considered for prevention and management, with the use of pharmacologic strategies approached with caution in patients with mood disorders. If a patient is at high risk of developing diabetes, certain pharmacologic agents have been shown to delay the onset of overt diabetes. Once diabetes or dyslipidemia are diagnosed, management should proceed in accordance with approved guidelines for these conditions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".