CT/CT Angiography and MRI Findings Predict Recurrent Stroke After Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transient ischemic attack and minor stroke portend a substantial risk of recurrent stroke. MRI can identify patients at high risk for a recurrent stroke. However, MRI is not commonly available as an emergency. If similarly clinically predictive, a CT/CT angiographic (CTA) imaging strategy would be more widely applicable. METHODS: Five hundred ten patients with consecutive transient ischemic attack and minor stroke underwent CT/CTA and subsequent MRI. We assessed the risk of recurrent stroke within 90 days using standard clinical variables and predefined abnormalities on the CT/CTA (acute ischemia on CT and/or intracranial or extracranial occlusion or stenosis ≥50%) and MRI (diffusion-weighted imaging-positive). RESULTS: There were 36 recurrent strokes (7.1%; 95% CI, 5.0-9.6). Median time to the event was 1 day (interquartile range, 7.5). Median time from onset to CTA was 5.5 hours (interquartile range, 6.4 hours) and to MRI was 17.5 hours (interquartile range, 12 hours). Symptoms ongoing at first assessment (hazard ratio, 2.2; 95% CI, 1.02-4.9), CT/CTA abnormalities (hazard ratio, 4.0; 95% CI, 2.0-8.5), and diffusion-weighted imaging positivity (hazard ratio, 2.2; 95% CI, 1.05-4.7) predicted recurrent stroke. In the multivariable analysis, only CT/CTA abnormalities predicted recurrent stroke. In a secondary analysis, CT/CTA and MRI were not significantly different in their discriminative value in predicting recurrent stroke (0.67; (95% CI, 0.59-0.76 versus 0.59; 95% CI, 0.52-0.67; P=0.09). CONCLUSIONS: Early assessment of the intracranial and extracranial vasculature using CT/CTA predicts recurrent stroke and clinical outcome in patients with transient ischemic attack and minor stroke. In many institutions, CTA is more readily available than MRI and physicians should access whichever technique is more quickly available at their institution.
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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) | 0.000 | 0.001 |
| 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 | 0.000 | 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".