Impact of Atrial Fibrillation on Outcome in Thrombolyzed Patients With Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Atrial fibrillation has been considered a risk factor for poor outcome from acute stroke and may influence response to thrombolysis, although supporting data are limited due to potential confounding with age and stroke severity. METHOD: We assessed the association of atrial fibrillation and thrombolysis exposure with the modified Rankin Scale score distribution at 90 days among patients registered in a trials archive. We used an age and baseline National Institutes of Health Stroke Scale-adjusted Cochran-Mantel-Haenszel test to test significance (P) followed by proportional odds logistic regression analysis to estimate the ORs for improved modified Rankin Scale score. RESULTS: Data were available for 7091 patients, of whom 3027 were thrombolyzed. A total of 1631 patients had a history of atrial fibrillation, of whom 639 were thrombolyzed. Among patients with atrial fibrillation, baseline severity was greater (median baseline National Institutes of Health Stroke Scale, 14 versus 12; P<0.001) and age was higher (mean age, 74.0 versus 66.5; P<0.001). An association of treatment with outcome was seen independently and was of similar magnitude within patients with atrial fibrillation (OR, 1.44; 95% CI, 1.12-1.73; P<0.001) and without atrial fibrillation (OR, 1.53; 95% CI, 1.39-1.69; P<0.001). No association of atrial fibrillation and overall stroke outcome could be found (OR, 0.93; 95% CI, 0.84-1.03; P=0.409). CONCLUSIONS: In this nonrandomized comparison, presence of atrial fibrillation had no independent impact on stroke outcome and compared with untreated comparators, the patients who received thrombolysis experienced an advantage in outcomes that was of equal magnitude whether in the presence or absence of 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".