Reduced risk of hospitalisation with risperidone long-acting injectable. Results of the french cohort for the general study of schizophrenia (CGS)
Bibliographic record
Abstract
Introduction Medication non-adherence is a significant risk factor for rehospitalisation in schizophrenia patients. Delayed release formulations like R-LAI may reduce rehospitalisation. Objectives To examine the association between R-LAI use and hospitalisation in schizophrenia patients. Aims To assess the effect of R-LAI, compared to non-use and use of other antipsychotic drugs, on the risk of hospitalization in real-life settings. Method The CGS study recruited schizophrenia patients from 177 public and private wards of psychiatric hospitals across France. Inclusion criteria were schizophrenia (DSM-IV), age 15–65 years, ambulatory/hospitalised for < 93 days at entry. Patients were followed up to 12 months for antipsychotic use and hospitalisation. The recruitment was stratified for long-acting second generation antipsychotic use. Multivariate Poisson regression adjusted for confounding with propensity scores and allowing for autocorrelation was used to estimate relative rates of hospitalisation. Results Of 2092 eligible patients, 1859 were included. Their mean age was 38.1 ± 11.1 years, 68.6% were male and 37.8% were hospitalised for < 93 days at entry. A total of 1659 patients (89.2%) were followed up for 12 months, accumulating 933 hospital stays (53.0 per 100 person-years). Compared to other schizophrenia patients, patients on R-LAI were younger, had more often a history of previous hospitalisation for equivalent severity, living conditions and other characteristics. The adjusted relative rate of hospitalisation for R-LAI use against non-use was 0.66 [95% CI 0.46–0.96], and 0.53 [95% CI 0.32–0.88] against long-acting first generation antipsychotics. Conclusions Use of R-LAI was associated with lower rates of hospitalization compared to non-use of R-LAI.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".