Impact of early use of long-acting injectable antipsychotics on psychotic relapses and hospitalizations in first-episode psychosis
Bibliographic record
Abstract
Early relapse is frequent in the first-episode psychosis (FEP), often because of poor adherence to medication. Previous studies have shown positive impacts of long-acting injectable antipsychotics (LAI-AP) on relapse rates, while others have discerned no differences. This study describes the impact of early LAI-AP utilization on relapse and rehospitalization rates in FEP. A three-year, longitudinal, prospective, naturalistic study of all admissions of psychosis patients for early intervention services was conducted. Four hundred sixteen patients were subdivided into four groups according to the route of antipsychotic administration. Patients who received LAI-AP as their first treatment modality were more likely to exhibit poor prognostic factors at baseline. However, their relapse rate over time was similar to those with good prognostic factors at baseline who only received oral antipsychotics (OAP). Patients who were initially prescribed OAP and eventually switched to LAI-AP were more likely to relapse and to be rehospitalized, even if they manifested better functioning at baseline than those started on LAI-AP. Patients with poor prognosis in the early stage of their disease seem to benefit from early prescription of LAI-AP which can reduce and delay relapses and rehospitalizations. Because they are often still at school or at work at the time of their first episode of psychosis, relapse prevention seems particularly relevant to avoid functional deterioration.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".