Electroconvulsive Therapy for Agitation in Schizophrenia: Metaanalysis of Randomized Controlled Trials.
Bibliographic record
Abstract
BACKGROUND: Agitation poses a significant challenge in the treatment of schizophrenia. Electroconvulsive therapy (ECT) is a fast, effective and safe treatment for a variety of psychiatric disorders, but no meta-analysis of ECT treatment for agitation in schizophrenia has yet been reported. AIMS: To systematically evaluate the efficacy and safety of ECT alone or ECT-antipsychotics (APs) combination for agitation in schizophrenia. METHODS: Systematic literature search of randomized controlled trials (RCTs) was performed. Two independent evaluators selected studies, extracted data about outcomes and safety with available data, conducted quality assessment and data synthesis. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) was used to judge the level of the overall evidence of main outcomes. RESULTS: =0.02). Headache (number-needed-to-harm (NNH)=3, 95%CI=2-4) was more frequent in the ECT alone group compared to AP monotherapy. According to the GRADE approach, the evidence levels of main outcomes were rated as ''very low'' (37.5%) and "low" (50%). CONCLUSION: Pooling of the data based on 7 RCTs from China found no advantage of ECT alone or ECT-APs combination in the treatment of agitation related outcomes in schizophrenia patients. However, ECT alone or ECT-APs combination were associated with significant reduction in the PANSS total score. High-quality RCTs are needed to confirm the current interpretations.
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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 | 0.025 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".