Adherence to clozapine vs. other antipsychotics in schizophrenia
Bibliographic record
Abstract
Background To date, there have been no studies evaluating adherence to clozapine with electronic adherence monitoring (EAM) such as the Medication Event Monitoring System (MEMS ® ). Methods In outpatients with schizophrenia, we conducted a 3‐month prospective study investigating antipsychotic adherence with EAM (eCAP ® ). Participants were treated with different oral antipsychotics, including clozapine, and blind to EAM monitoring; all were on antipsychotic monotherapy administered once daily. Outcome measures included adherence rate, missed dose, and medication gap. Adherence trajectory patterns were also analyzed for clozapine vs. other antipsychotics collectively. Results A total of 111 patients were included in the study; 33 and 78 patients received clozapine or other antipsychotics, respectively. Adherence rates, defined as proportion of days that the subject took the medication at the prescribed time ± 3 h and proportion of subjects with ≥80% adherence, were numerically higher in patients receiving clozapine vs. other antipsychotics (72.0% vs. 65.1%, P = 0.10; 49.5% vs. 35.7%, P = 0.11, respectively). Along similar lines, some of the missed dose and medication gap outcomes were significantly better in patients receiving clozapine vs. other antipsychotics. Three adherence trajectory patterns were identified for both clozapine and other antipsychotics, with two shared by both groups (i.e., low adherence with a slight decrease over time; high and stable adherence). Conclusion Findings suggest that in patients with schizophrenia clozapine adherence is at least comparable, if not slightly better, compared with other antipsychotics.
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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 | 0.001 | 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) | 0.001 | 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".