Choice of Oral Anticoagulant: Outcomes in Atrial Fibrillation Patients Post-Stroke Despite Direct Oral Anticoagulant Use
Bibliographic record
Abstract
Background: For patients with atrial fibrillation who have an ischemic stroke or transient ischemic attack (TIA) despite taking direct oral anticoagulants (DOACs), the optimal strategy for ongoing anticoagulation is unknown. Methods: Using provincial administrative databases in Alberta, Canada, we compared anticoagulant use before/after the breakthrough stroke/TIA and assessed recurrence of stroke/TIA or bleeding, with consideration of medication adherence. Adherence was defined as the proportion of days covered (PDC) being ≥ 80%. Results: score of 1.7± 1 prior to the index event. Patients were followed for a median of 643 days (interquartile range 836). Following the index stroke/TIA event, 623 patients (63%) filled a prescription for the same DOAC regimen, 83 (8%) filled a prescription for a different dose, 155 (16%) switched DOAC agents, 51 (5%) switched to warfarin, and 73 (7%) filled no oral anticoagulant prescription. Patients who kept the same regimen more commonly had TIA index events (59%); patients who changed dose or drug more often had stroke index events (55%-78%). During follow-up, 135 (14%) had stroke/TIA recurrence, and 46 (5%) had bleeding; rates of each did not differ between prescribing patterns. Post-index event, the proportion of patients with a proportion of days covered ≥ 80% improved from 55% to 80%. Conclusions: Although most maintained the same DOAC regimen after stroke/TIA, rates of recurrent stroke/TIA and bleeding were similar across prescribing patterns. Stroke/TIA severity may have influenced prescribing practices. DOAC prescription adherence improved poststroke/TIA and signals an opportunity for optimization in patients with atrial fibrillation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".