Predictors of Treatment Discontinuation and Medication Nonadherence in Patients Recovering From a First Episode of Schizophrenia, Schizophreniform Disorder, or Schizoaffective Disorder
Bibliographic record
Abstract
OBJECTIVE: To evaluate predictors of treatment discontinuation against medical advice and poor medication adherence among first-episode patients treated with olanzapine, quetiapine, or risperidone. METHOD: First-episode patients with schizophrenia, schizophreniform disorder, or schizoaffective disorder (DSM-IV) were randomly assigned to olanzapine (2.5-20 mg/day), quetiapine (100-800 mg/day), or risperidone (0.5-4 mg/day) as part of a 52-week, randomized, double-blind, flexible-dose, multicenter study. Patients were enrolled from 2002 to 2004 at one of 26 sites in the United States and Canada. Survival analysis tested for predictors of treatment discontinuation against medical advice, while mixed models tested for predictors of poor medication adherence. Significant findings from the final models were replicated in sensitivity analyses. RESULTS: Of the 400 patients randomly assigned to treatment, 115 patients who discontinued treatment against medical advice and 119 study completers were compared in this analysis. Poor treatment response (p < .001) and low medication adherence (p = .02) were independent predictors of discontinuation against medical advice. Ongoing substance abuse, ongoing depression, and treatment response failure significantly predicted poor medication adherence (p < .01). Higher cognitive performance at baseline and ethnicity (black) were also associated with lower medication adherence (p < .05). An association between poor medication adherence and illness insight at study entry was found at trend level (p = .059). CONCLUSION: This study highlights the importance of treatment response in predicting discontinuation against medical advice and poor adherence to medication in first-episode patients. These results also support interventions to improve adherence behavior, particularly by targeting substance use disorders and depressive symptoms. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT00034892 (http://www.clinicaltrials.gov).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".