Bibliographic record
Abstract
CADTH recommends that Vraylar not be reimbursed by public drug plans for the treatment of schizophrenia in adults. Vraylar was discussed at the March 2022 meeting of the CADTH Canadian Drug Expert Committee (CDEC) and reconsidered at the July 2022 meeting. Based on evidence from 5 clinical trials, treatment with Vraylar improved symptoms of schizophrenia or delayed relapse compared with placebo. Vraylar also improved negative symptoms of schizophrenia compared with risperidone. Although these results were statistically significant, it is not clear whether any of these effects result in a clinically meaningful improvement in patient-identified needs. It is not clear whether cariprazine offers any clinical benefits over other treatments that are available for schizophrenia because there were no clinical trials in patients with acute schizophrenia that compared Vraylar with any other treatments. The committee did not have confidence in the results of the indirect comparative evidence because it had too many limitations. There was not enough robust evidence to show that Vraylar filled a treatment gap. This document was initially published on August 26, 2022, and subsequently revised on February 2, 2023, to provide additional details on the expert committee's deliberations and the request for reconsideration filed by the sponsor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".