Impact of Restrictive Prescription Plans on Heart Failure Medication Use
Bibliographic record
Abstract
BACKGROUND: Prescription plans frequently use restrictive strategies to control drug expenditures. Increased restrictions may reduce access to evidence-based therapy among patients with chronic disease. We sought to evaluate the impact of increased restrictions on medication use among heart failure (HF) patients. METHODS AND RESULTS: We conducted a population-based cohort study of administrative data from 3 Canadian provinces. During 1998 to 2001, Quebec (QC) had a minimally restrictive plan, whereas Ontario (ON) and British Columbia (BC) had more restrictive prescription plans. We evaluated drug use at 30 days of discharge stratified by prescription plan. Provincial rates of filled prescriptions for HF drugs in QC, ON, and BC were 62%, 58%, and 47% for angiotensin-converting enzyme inhibitors; 34%, 22%, and 16% for beta-blockers; 9%, 5%, and 3% for angiotensin receptor blockers; and 79%, 76%, and 62% for loop diuretics, respectively. In multivariate analyses, patients residing in provinces with restrictive plans were less likely to be prescribed drugs that were restricted, such as beta-blockers (odds ratio, 0.53; 95% CI, 0.46 to 0.60; 0.36, 0.29 to 0.44, for ON and BC, respectively) and angiotensin receptor blockers (0.50, 0.45 to 0.56; 0.38, 0.32 to 0.46, for ON and BC, respectively), than drugs with no restrictions, such as loop diuretics (0.81, 0.74 to 0.88; 0.40, 0.36 to 0.45, for ON and BC, respectively) and angiotensin-converting enzyme inhibitors (0.80, 0.75 to 0.86; 0.47, 0.43 to 0.52, for ON and BC, respectively). CONCLUSIONS: Among HF patients, residing in a province with a more restrictive prescription plan may be associated with lower use of restricted HF medications over and above the expected regional differences in HF drug use across provinces.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".