Medication Gaps and Antipsychotic Polypharmacy in Previously Hospitalized Schizophrenia Patients: An Electronic Cohort Study in Three Canadian Provinces
Bibliographic record
Abstract
Background: Real world evidence about antipsychotics focuses on rehospitalization. Modeling the time course of pharmacotherapy would show patients' adherence to medications and physicians' adherence to medication guidelines. We aimed to calculate the cumulative time spent in second generation antipsychotics (SGAs), gaps, antipsychotic polypharmacy, and clozapine in discharged schizophrenia patients. Methods: = 2,997) was created (mean follow-up: 49 months, SD = 38). Cohort members were required to have a minimum of 6 weeks medicated with aripiprazole, olanzapine, paliperidone, quetiapine, risperidone, or ziprasidone. Results: The multistate model predicted that schizophrenia patients accumulated 44 months in SGA monotherapy, 4 months in polypharmacy, 11 months in medication gaps and 17 days in clozapine over a 5-year period. The majority of transitions were between SGA and medication gap. Accumulated time in medication gaps was seven times as much as in clozapine. Each 10% delay in SGA initiation post-discharge was associated with a 2, 1, and 6% higher risk for polypharmacy (95% CI: 1.01-1.02), gap (95% CI: 1.01-1.01), and clozapine (95% CI: 1.04-1.08), respectively. Interpretation: Schizophrenia patients accumulated more time unmedicated and in polypharmacy compared to clozapine. Either treatment guidelines for schizophrenia are not followed, or real-world challenges hamper their implementation.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".