Factors associated with relapse in schizophrenia despite adherence to long-acting injectable antipsychotic therapy
Bibliographic record
Abstract
Many patients with schizophrenia will relapse despite uninterrupted antipsychotic (AP) long-acting therapy (LAT). This exploratory analysis examined variables associated with relapse despite ensured adherence to LAT. This was a post-hoc exploratory analysis of a 1-year study of risperidone long-acting injection in patients with stable schizophrenia or schizoaffective disorder (NCT00297388; N=323). Patients were discontinued from previous oral APs and randomly assigned to biweekly intramuscular injections of risperidone long-acting injectable 50 (n=163) or 25 mg (n=161) for 52 weeks. Cox proportional hazards regression models examined variables putatively associated with relapse. A total of 59/323 (18.3%) patients relapsed over 12 months despite continuous AP LAT. Variables associated with the risk of relapse included illness duration (6.0% increase each year; P=0.0003) and country (Canada vs. USA, 4.7-fold risk increase; P=0.0008). When illness duration was further categorized as ≤5, 6-10, and >10 years, patients with an illness duration of >10 versus ≤5 years were at greatest risk of relapse (>10 vs. ≤5 years associated with a 4.4-fold increase in the risk of relapse; P=0.0181). Findings suggest that patients with more chronic illness have a greater risk of relapse despite ensured treatment adherence, supporting the need for early intervention to prevent the deleterious effects of chronicity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".