Association between previous and future antipsychotic adherence in patients initiating clozapine: real-world observational study
Bibliographic record
Abstract
BACKGROUND: Although recognised as the most effective antipsychotic for treatment-resistant schizophrenia, clozapine remains underused. One reason is the widespread concern about non-adherence to clozapine because of poor adherence before initiating clozapine. AIMS: To determine if prior poor out-patient adherence to treatmentbefore initiating clozapine predisposes to poor out-patient adherence to clozapine or to any antipsychotics (including clozapine) after its initiation. METHOD: This cohort study included 3228 patients with schizophrenia living in Quebec (Canada) initiating (with a 2-year clearance period) oral clozapine (index date) between 2009 and 2016. Using pharmacy data, out-patient adherence to treatment was measured by the medication possession ratio (MPR), over a 1-year period preceding and following the index date. Five groups of patients were formed based on their prior MPR level (independent variable). Two dependent variables were defined after clozapine initiation (good out-patient adherence to any antipsychotics and to clozapine only). Along with multiple logistic regressions, state sequence analysis was used as a visual representation of antipsychotic-use trajectories over time, before and after clozapine initiation. RESULTS: Although prior poor adherence to antipsychotics was associated with poor adherence after clozapine initiation, the absolute risk of subsequent poor adherence remained low, regardless of previous adherence level. Most patients adhered to their treatment after initiating clozapine (>68% to clozapine and >84% to any antipsychotics). CONCLUSIONS: Despite the fact that poor adherence prior to initiating clozapine is widely recognised by clinicians as a barrier for the prescription of clozapine, the current study supports the initiation of clozapine in all eligible patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".