Impact of schizophrenia relapse definition on the comparative effectiveness of oral versus injectable antipsychotics: A systematic review and meta‐analysis of observational studies
Bibliographic record
Abstract
Abstract Although relapse is an important outcome to measure the effectiveness of schizophrenia treatment, no standard definition exists. This review aimed at identifying definitions and measurements of schizophrenia relapse in observational studies of long‐acting injectables (LAIs) versus oral antipsychotics (OAPs) and at determining their impact on heterogeneity of comparative effectiveness estimates. A systematic review was conducted using MEDLINE and Embase (01 January 2010–11 November 2019 [date last searched]). Pragmatic searches of gray literature and snowballing were also conducted. Search outputs were screened independently by two assessors at first stage, and full‐text of potentially eligible sources at second stage. For each retained source, definition and measurement of relapse, study methods, and comparative effectiveness estimates were extracted. Heterogeneity of estimates was assessed using I2 statistic with a threshold of 50% for substantial heterogeneity. Literature search yielded 543 sources and pragmatic searches, 21, of which 35 were eligible. Twelve definitions of relapse were found based on hospitalization/emergency department (ED) data (28 studies) or clinical assessment (5 studies). No definition was provided in five studies. According to quantitative analyses, in studies defining relapse as schizophrenia‐related hospitalization and/or ED visits over 1‐year follow‐up, LAIs were significantly more effective than OAPs. For studies measuring relapse based on all‐cause hospitalization, heterogeneity was too high for pooling; yet this definition is the most frequently found in pooled estimates published in the literature. Schizophrenia relapse definitions led to substantial heterogeneity of comparative effectiveness estimates of LAIs versus OAPs. Creating study subgroups based on relapse definition effectively reduces statistical heterogeneity.
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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 | 0.068 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".