Medication-Adherent First-Episode Psychosis Patients Also Relapse: Why?
Bibliographic record
Abstract
OBJECTIVE: Poor adherence to medication is a major determinant of relapse following treatment of first-episode psychosis (FEP). However, medication-adherent patients also relapse. We examined what factors influence the risk of relapse after controlling for adherence. METHOD: We selected a sample of fully adherent patients (n = 65) who had achieved remission at one point. We then compared patients who relapsed, using 2 different definitions of relapse, to those who did not relapse by 12 months on age, sex, premorbid adjustment, duration of untreated psychosis, length of prodrome, and substance abuse. RESULTS: Among the 65 medication-adherent patients in remission, 9 (14%) relapsed according to criteria for relapse requiring a change in medication. These patients differed from those who remained in remission only in the pattern of premorbid adjustment (greater proportion with deteriorating pattern), although this was not independent of other variables. No differences were found on any other variable. Using a more commonly used metric for relapse, based on symptom ratings alone, an additional 14 (21.5%) patients relapsed. Substance abuse significantly predicted relapse, with substance abusers having more than 25 times the odds of relapsing by 12 months (OR 25.6; 95% CI 2.4 to 278.1, P = 0.008). CONCLUSION: Using a more conservative definition of relapse in this adherent-to-medication population, we find a very low rate of relapse associated, at least partially, with poor premorbid adjustment. As substance abuse was a significant predictor of symptomatic relapse, this would suggest that there should be a greater emphasis on interventions focused on reducing substance abuse in FEP.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".