Three-Year Naturalistic Study On Early Use Of Long-Acting Injectable Antipsychotics In First Episode Psychosis
Bibliographic record
Abstract
Abstract: Poor adherence to antipsychotics, which affects outcome, is frequent in first episode psychosis (FEP). Most randomized studies demonstrate no superiority of long-acting injectable antipsychotics (LAI-AP) over oral antipsychotics (OAP). However, participants in these studies represent a minority of patients who may benefit from LAI-AP. Mirror and naturalistic studies generally demonstrate efficacy of LAI-AP on more representative samples, but studies on FEP are scarce. Aim: To describe LAI-AP's utilization and impact on FEP outcome in a naturalistic setting. Methods: A 3-year longitudinal prospective and retrospective descriptive study of all consecutive admissions from two Early Intervention Services for psychosis (EIS) in Montréal, Canada, compared the characteristics and evolution of patients who received LAI-AP for at least 12 months to those who received OAP only. Results: From 375 FEP patients included, 26,7% received LAI-AP during their follow-up. They were more likely to have poor prognostic factors (male gender, lower premorbid functioning, homelessness, substance use disorder and schizophrenia spectrum diagnoses). Despite a more severe illness and lower functioning in the LAI-AP group, at admission and study endpoint, clinical and functional improvements were observed. Conclusion: Early prescription of LAI-AP seems beneficial in FEP with poor prognostic factors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".