Bridging intravenous thrombolysis in patients with atrial fibrillation
Bibliographic record
Abstract
Background and purpose 40% of acute ischemic stroke patients treated by mechanical thrombectomy (MT) have a clinical history of atrial fibrillation (AF). The safety of bridging intravenous thrombolysis (IVT) (MT + IVT) is currently being discussed. We aimed to analyze the interaction between oral anticoagulation (OAC) status or AF with bridging IVT, regarding the occurrence of symptomatic intracranial hemorrhage (sICH) and functional outcome. Materials and Methods Multicentric observational cohort study (BEYOND-SWIFT registry) of consecutive patients undergoing MT between 2010 and 2018 (n = 2,941). Multinomial regression models were adjusted for prespecified baseline and plausible pathophysiological covariates identified on a univariate analysis to assess the association of AF and OAC status with sICH and good outcomes (90-day modified Rankin Scale score 0–2). Results In the total cohort (median age 74, 50.6% women), 1,347 (45.8%) patients had AF. Higher admission National Institutes of Health Stroke Scale (NIHSS) score (aOR 1.04 [95% 1.02–1.06], per point of increase) and prior medication with Vitamin K antagonists (VKA) (aOR 2.19 [95% 1.27–3.66]) were associated with sICH. Neither AF itself (aOR 0.71 [95% 0.41–1.24]) nor bridging IVT (aOR 1.08 [0.67–1.75]) were significantly associated with increased sICH. Receiving bridging IVT (aOR 1.61 [95% 1.24–2.11]) was associated with good 90-day outcome, with no interaction between AF and IVT (p = 0.92). Conclusion Bridging IVT appears to be a reasonable clinical option in selected patients with AF. Given the increased sICH risk in patients with VKA, subgroup analysis of the randomized controlled trials should analyze whether patients with VKA might benefit from withholding bridging IVT. Registration clinicaltrials.gov ; Unique identifier: NCT03496064.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".