Efficacy of long‐acting injectable versus oral antipsychotic drugs in early psychosis: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Long-acting injectable antipsychotic drugs (LAIs) are often used as an alternative to oral antipsychotics (OAPs) in individuals with psychosis who demonstrate poor medication adherence. Previous meta-analyses have found mixed results on the efficacy of LAIs, compared to OAPs, in patients with psychotic disorders. The objective of this meta-analysis was to compare the effectiveness of using LAIs versus OAPs in the early stages of psychosis. METHODS: Major electronic databases were used to search for any studies examining the comparative effectiveness (i.e., relapse, adherence, hospitalization, and all-cause discontinuation) of any LAIs versus OAPs in early stages of psychosis. Studies published up to 6 June, 2019 were included and no language restriction was applied. Inclusion criteria were a diagnosis of schizophrenia or related disorder, where patients were in their first episode or had a duration of illness ≤5 years. Data were analysed using a random-effects model. RESULTS: Fifteen studies (n = 10 584) were included, of which were 7 RCTs, 7 observational studies, and 1 post-hoc analysis. We found that LAIs provided advantages over OAPs in terms of relapse rates. No significant differences were found between LAI and OAP groups in terms of all-cause discontinuation, hospitalization, and adherence rates. However, considering only RCTs revealed advantages of LAIs over OAPs in terms of hospitalization rates. CONCLUSIONS: LAIs may provide benefits over OAPs with respect to reducing relapse and hospitalization rates in early psychosis patients. There is a need for larger and better-designed studies comparing OAPs and LAIs specifically in early psychosis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.006 | 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) | 0.003 | 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".