Aligning Health Care Policy With Evidence-Based Medicine: The Case for Funding Direct Oral Anticoagulants in Atrial Fibrillation
Bibliographic record
Abstract
Misalignment between evidence-informed clinical care guideline recommendations and reimbursement policy has created care gaps that lead to suboptimal outcomes for patients denied access to guideline-based therapies. The purpose of this article is to make the case for addressing this growing access barrier to optimal care. Stroke prevention in atrial fibrillation (AF) is discussed as an example. Stroke is an extremely costly disease, imposing a significant human, societal, and economic burden. Stroke in the setting of AF carries an 80% probability of death or disability. Although two-thirds of these strokes are preventable with appropriate anticoagulation, this has historically been underprescribed and poorly managed. National and international guidelines endorse the direct oral anticoagulants as first-line therapy for this indication. However, no Canadian province has provided these agents with an unrestricted listing. These decisions appear to be founded on silo-based cost assessment-the drug costs rather than the total system costs-and thus overlook several important cost-drivers in stroke. The discordance between best scientific evidence and public policy requires health care providers to use a potentially suboptimal therapy in contravention of guideline recommendations. It represents a significant obstacle for knowledge translation efforts that aim to increase the appropriate anticoagulation of Canadians with AF. As health care professionals, we have a responsibility to our patients to engage with policy-makers in addressing and resolving this barrier to optimal patient care.
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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 | 0.274 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.037 | 0.029 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.058 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".