Systematic review of long-acting injectables versus oral atypical antipsychotics on hospitalization in schizophrenia
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of long-acting injectables (LAIs) versus oral antipsychotics (OAs) on hospitalizations among patients with schizophrenia by conducting a systematic literature review of studies with different study designs and performing a meta-analysis. METHODS: Using the PubMed database and major psychiatric conference proceedings, a systematic literature review for January 2000 to July 2013 was performed to identify English-language studies evaluating schizophrenia patients treated with atypical antipsychotics. Studies reporting hospitalization rates as a percentage of patients hospitalized or as the number of hospitalizations per person per year were selected. The primary meta-analysis assessed the percentage decrease in hospitalization rates before and after treatment initiation for matched time periods. The secondary meta-analysis assessed the absolute rate of hospitalization during follow-up. Pooled treatment-effect estimates were calculated using random-effects models. To account for differences in patient and study-level characteristics between studies, meta-regression analyses were used. Subset analyses further explored the heterogeneity across study designs. RESULTS: Fifty-eight studies evaluating 25 arms (LAIs: 13 arms, 4516 patients; OAs: 12 arms, 23,516 patients) in the primary meta-analysis and 78 arms (LAIs: 12 arms, 4481 patients; OAs: 66 arms, 96,230 patients) in the secondary meta-analysis were identified. Reduction in hospitalization rates for LAIs was 20.7 percentage points higher than that of OAs (random-effects estimates: LAIs = 56.2% vs. OAs = 35.5%, P = 0.023). Controlling for patient and study characteristics, the adjusted percentage reduction in hospitalization rates for LAIs was 26.4 percentage points higher than for OAs (95% CI: 3.3-49.5, P = 0.027). As for the secondary meta-analysis, no significant difference between LAIs and OAs was observed (random-effects estimate: -8.6, 95% CI: -18.1-1.0, P = 0.077). Subset analyses across type of study yielded consistent results. Limitations of this analysis include the long observation period, which may not reflect current treatment patterns, the use of all-cause hospitalization, which may not be solely related to schizophrenia, and the fact that most studies in the LAI cohort evaluated risperidone. CONCLUSION: The primary results of this meta-analysis, including studies with both interventional and non-interventional designs and using meta-regressions, suggest that LAIs are associated with higher reductions in hospitalization rates for schizophrenia patients compared to OAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".