Can the Present Canadian Health Care System Provide Evidence-Based Pharmaceutical Care? Consideration of Two Important Cardiovascular Clinical Contexts
Bibliographic record
Abstract
BACKGROUND: Canada is the only country with universal health care lacking universal pharmaceutical care (pharmacare). Currently, a minority portion of the population has drug coverage. Furthermore, there may be discordance between provincial plans of medications that are covered. We aimed to evaluate present systems for drug coverage and ability to provide evidence-based care according to Canadian cardiovascular (CV) guidelines. METHODS: The drug review process and formularies for provinces and territories were queried to evaluate concordance between guideline-recommended medications and present coverage. Two common cardiology scenarios, heart failure with reduced ejection fraction and antiplatelet drugs in acute coronary syndrome, were chosen as case studies because of recent Canadian-based CV practice guideline updates. Coverage criteria were extracted to assess for interprovincial variations and determine whether listing decisions are concordant with best evidence and guidelines. RESULTS: Multiple organisations are involved in drug review. Despite Canada's Drug and Health Technology Agency (CADTH) recommendations, there are extensive interprovincial variations in listing decisions for CV drugs, with CADTH recommendations followed only 33% of the time. Among 24 CV drugs reviewed, 23% of listing decisions were discordant from guidelines and best evidence. Novel drugs disproportionately carried the highest discordance from evidence. No systematic process exists for formulary updates based on new evidence, changes in guidelines, or drug pricing. CONCLUSIONS: Current Canadian drug review decisions are complex and coverage decisions vary widely, resulting in an inability to provide guideline-recommended CV evidence-based medicine. In developing future pharmacare programs, it is crucial to centralise the drug review process and integrate mechanisms to incorporate up-to-date evidence.
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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 | 0.036 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".