The impact of weight gain associated with atypical antipsychotic use in schizophrenia
Bibliographic record
Abstract
BACKGROUND: Atypical antipsychotics offer clear advantages in the management of schizophrenia, compared with conventional neuroleptics, but weight gain is a significant adverse effect with some of these agents. OBJECTIVE: To review the literature on weight gain associated with atypical antipsychotic treatment in schizophrenia. METHODS: Relevant sources were identified from Medline searches to February 2003 using combinations of keywords including 'schizophrenia', 'antipsychotics', 'weight gain', 'adverse events', 'obesity', and 'diabetes'. RESULTS: Most atypical antipsychotics induce some weight gain, but the magnitude of the effect varies markedly. The greatest increases are seen with clozapine and olanzapine: risperidone has a slight effect, comparable with that of conventional neuroleptics, while ziprasidone and aripiprazole appear from current data to have little effect. In addition, atypical antipsychotics have been associated with metabolic disturbances, particularly glucose dysregulation and dyslipidemia. These effects tend to be more marked with olanzapine and clozapine than with other agents. Weight gain associated with atypical antipsychotics imposes substantial morbidity, in addition to that associated with schizophrenia itself. Furthermore, weight gain can significantly impair patients' quality of life, and leads to non-adherence with treatment. Effective weight management should include the selection of an appropriate atypical antipsychotic and for effective weight management, as well both diet and exercise, formal weight management programs tailored to the needs of schizophrenic patients may be useful, and some patients may benefit from weight-reducing drugs. CONCLUSIONS: Weight gain associated with atypical antipsychotics is a common problem that requires effective management. The selection of an agent with a low risk of weight gain, such as risperidone or ziprasidone, is central to such management.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".