Pathophysiological Determinants of Worse Stroke Outcome in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: The reasons for worse outcome following ischemic stroke in patients with atrial fibrillation (AF) remain unclear. We aimed to elucidate the pathophysiological determinants of poorer stroke outcome in patients with AF using systematic MRI data from the Echoplanar Imaging Thrombolytic Evaluation Trial (EPITHET). METHODS: Comparisons of infarct size, hypoperfusion volume, infarct growth, arterial occlusion, recanalization, reperfusion, hemorrhagic transformation and stroke severity were made between patients with and without AF enrolled in the EPITHET study. RESULTS: AF was present in 42 of 101 patients. At baseline, AF patients were older (79 vs. 73 years, p = 0.02), had more severe neurological impairment (National Institutes of Health Stroke Scale score 16 vs. 11, p = 0.006), larger infarcts (29 vs. 15 ml, p = 0.04) and greater volumes of more severe hypoperfusion (T(max) > or =8 s, perfusion-weighted imaging volume 70 vs. 43 ml, p = 0.01) compared to patients without AF. There were no significant differences in arterial occlusion site, infarct growth, recanalization or reperfusion. At outcome, AF patients had larger infarcts (52 vs. 16 ml, p = 0.05), more severe hemorrhagic transformation (29 vs. 5%, p = 0.002 for parenchymal hematomas), greater disability (modified Rankin Scale score 4 vs. 3, p = 0.03) and higher mortality rates (31 vs. 12%, p = 0.04). AF was an independent predictor of parenchymal hematoma (OR = 6.90, 95% CI = 1.57-30.25), but not mortality (OR = 2.56, 95% CI = 0.83-7.85). CONCLUSIONS: Patients with AF have worse clinical and imaging outcomes following ischemic stroke. This study suggests that the adverse effect of AF is due to greater volumes of more severely hypoperfused tissue, leading to larger infarct size and greater risk of severe hemorrhagic transformation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".