Newly Diagnosed Atrial Fibrillation After Transient Ischemic Attack Versus Minor Ischemic Stroke in the POINT Trial
Bibliographic record
Abstract
Background Atrial fibrillation/flutter (AF) after transient ischemic attack (TIA) has not been well studied. We compared the likelihood of new AF diagnosis after ischemic stroke versus TIA. Methods and Results The POINT (Platelet‐Oriented Inhibition in New TIA and Minor Ischemic Stroke) trial enrolled adults within 12 hours of minor ischemic stroke or high‐risk TIA. Our exposure was index event type (ischemic stroke versus TIA). The primary analysis used the original trial definition of TIA (resolution of symptoms/signs). In secondary analyses, TIA cases with infarction on neuroimaging were reclassified as strokes. Our primary outcome was a new AF diagnosis, ascertained from adverse event and treatment interruption/discontinuation reports. We calculated C‐statistics for variables associated with newly diagnosed AF. We used Kaplan‐Meier survival statistics and Cox models adjusted for demographics and vascular risk factors. Excluding 49 subjects with baseline AF, 2746 patients had index stroke and 2086 patients had index TIA. During the 90‐day follow‐up, 106 patients had newly diagnosed AF. Cumulative risks of AF were 2.7% (95% CI, 2.1%–3.4%) after stroke and 2.0% (95% CI, 1.5%–2.7%) after TIA ( P =0.15). After reclassifying index events by neuroimaging, cumulative AF risk was higher after stroke (2.7%; 95% CI, 2.2%–3.4%) than TIA (1.8%; 95% CI, 1.3%–2.5%) ( P =0.04). Index event type had negligible predictive utility (C‐statistic, 0.54). Conclusions Among patients with cerebral ischemia, the distinction between TIA versus minor stroke did not stratify the risk of subsequent AF diagnosis, implying that patients with TIA should undergo similar heart‐rhythm monitoring strategies as patients with ischemic stroke.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".