Anticoagulation Strategies Following Breakthrough Ischemic Stroke While on Direct Anticoagulants
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The management of anticoagulation after ischemic stroke while on direct oral anticoagulants (DOACs) is controversial. We performed an aggregate-data meta-analysis to compare anticoagulation strategies against each other to define the effect of switch to warfarin, switch to another DOAC, change in dosage, and add-on antiplatelet for the prevention of recurrent stroke, intracranial hemorrhage (ICH), any stroke, and mortality. METHODS: The study protocol was deposited with PROSPERO (CRD42025639057). We systematically searched MEDLINE, Scopus, and the Cochrane Library-all studies reporting on anticoagulation strategies after a stroke while on DOAC up to January 31, 2025. We included randomized controlled clinical studies and cohort studies with sample size ≥50 that (1) enrolled adult patients who experienced ischemic stroke while on DOACs, (2) assessed modifications to anticoagulation therapy, and (3) reported on at least one of the outcomes. Main outcome was recurrent ischemic stroke; secondary outcomes were ICH, all-cause mortality, and any stroke. We pooled estimates by random-effects modelling, reporting risk ratio (RR) with 95% CIs comparing anticoagulation strategies against each other. RESULTS: = 5). Keeping the same DOAC and switching to another DOAC, independently from mechanism, had similar rates of primary and secondary outcomes. DISCUSSION: Our meta-analysis indicates that switching to warfarin after a stroke while on DOAC seems less effective and safe in stroke recurrence prevention, ICH, and mortality compared with DOAC-based strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.034 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".