Atrial Fibrillation in Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Atrial fibrillation (AF) increases the risk of stroke and is associated with poor stroke outcomes. Limited tools are available to evaluate clinical outcomes and response to thrombolysis in stroke patients with AF. METHODS: We applied the iScore (http://www.sorcan.ca/iscore), a validated risk score, to consecutive patients with an acute ischemic stroke admitted to stroke centers in the Registry of the Canadian Stroke Network. The main outcome considered was a favorable outcome (defined as a modified Rankin scale 0-2) at discharge after thrombolysis. Secondary outcomes included intracerebral hemorrhage, death at 30 days, and at 1 year stratified by terciles of the iScore. RESULTS: Among 12 686 patients with an acute ischemic stroke, 2185 (17.2%) had AF. Overall, AF patients had higher risk of death at 30 days (22.3% versus 10.2%; P<0.0001), 1 year (37.1% versus 19.5%; P<0.0001) and death or disability at discharge (69.7% versus 54.7%; P<0.0001) compared with non-AF patients. After adjustment, thrombolysis was associated with a favorable outcome for patients without AF (relative risk, 1.18; 95% CI, 1.10-1.27), but no benefit was observed for patients with AF (relative risk, 0.91; 95% CI, 0.71-1.17). There was a modestly increased risk of intracranial hemorrhage (any type) (16.5% versus 11.6%; relative risk, 1.42; 95% CI, 1.05-1.91) after thrombolysis among AF compared with non-AF patients. In the logistic regression analysis, there was an interaction between tPA and iScore for a favorable outcome (P-value interaction <0.001). The interaction also was significant (P<0.0012) among patients without AF, but did not reach significance (P=0.17) in patients with AF. CONCLUSIONS: Stroke patients with AF have higher mortality, greater risk of intracerebral hemorrhage, and a similar response trend to thrombolysis compared with non-AF patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".