Adherence to direct or vitamin K antagonist oral anticoagulants in patients with atrial fibrillation: a long-term observational study
Bibliographic record
Abstract
Our objectives were to measure long-term adherence to oral anticoagulants (OACs) in patients with atrial fibrillation (AF) and to identify patient factors associated with adherence. Using linked, population-based administrative data from British Columbia, Canada, an incident cohort of adults prescribed OACs for AF was identified. We calculated the proportion of days covered (PDC) as a time-dependent covariate for each 90-day window from OAC initiation until the end of follow-up. Associations between patient attributes and adherence were assessed using generalized mixed effect linear regression models. 30,264 patients were included. Mean PDC was 0.69 (SD 0.28) over a median follow-up of 6.7 years. 54% of patients were non-adherent (PDC < 0.8). After controlling for confounders, factors positively associated with adherence were number of drug class switches, history of stroke or transient ischemic attack, history of vascular disease, time since initiation, and age. Age > 75 years at initiation, polypharmacy (among VKA users only), and receiving DOAC (vs. VKA) were negatively associated with adherence. PDC decreased over time for VKA users and increased for DOAC users. Over half of AF patients studied were, on average, nonadherent to OAC therapy and missed 32% of their doses. Several patient factors were associated with higher or lower adherence, and adherence to VKA declined during therapy while DOAC adherence increased slightly over time. To min im ize the risk stroke, adherence-supporting interventions are needed for all patients with AF, particularly those aged > 75 years, those with prior stroke or vascular disease, VKA users with polypharmacy, and DOAC recipients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".