Critical review of antipsychotic polypharmacy in the treatment of schizophrenia
Bibliographic record
Abstract
Antipsychotic polypharmacy remains prevalent; it has probably increased for the treatment of schizophrenia in real-world clinical settings. The current evidence suggests some clinical benefits of antipsychotic polypharmacy, such as better symptom control with clozapine plus another antipsychotic, and a reversal of metabolic side-effects with a concomitant use of aripiprazole. On the other hand, the interpretation of findings in the literature should be made conservatively in light of the paucity of good studies and potentially serious side-effects. Also, although the available data are still limited, two smaller-scale clinical trials provide preliminary evidence that converting antipsychotic polypharmacy to monotherapy could be a valid and reasonable treatment option. Several studies have explored strategies to change physicians' antipsychotic polypharmacy prescribing behaviours. These have revealed that, while the impact of purely educational interventions may be limited, more aggressive procedures such as directly notifying physicians by letters or phone calls can be more effective in reducing antipsychotic polypharmacy. In conclusion, antipsychotic polypharmacy can work for some clinically difficult conditions; however, it should be the exception rather than the rule and may be avoidable in many patients. More importantly, the paucity of the data clearly emphasizes the need for further investigations on not only advantages and disadvantages of antipsychotic polypharmacy, but also regarding effective interventions in already prescribed polypharmacy regimens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".