Abstract WP56: Refinement of imaging predictors of recurrent events following Transient Ischemic Attack and Minor Stroke.
Bibliographic record
Abstract
Background: Transient ischemic attack (TIA) and minor stroke have a high risk of recurrent stroke. We recently showed in the CATCH study that predefined radiographic abnormalities on CT/CTA and MRI predicted recurrent events after TIA and minor stroke. Specifically, the study recognized the predictive value of CT/CTA abnormalities that were defined apriori: acute ischemia on CT, intracranial or extracranial occlusion or stenosis > 50% (the CT/CTA positive metric), and diffusion-weighted imaging positivity on MRI. Aims: To improve upon the CT, CTA, MRI and clinical parameters that predict recurrent events after TIA and minor stroke. Our secondary aim was to explore predictors of stroke progression versus recurrence. Methods: 510 consecutive TIA and minor stroke patients (NIHSS score of <4) had CT/CTA and most had MRI. Primary outcome was recurrent events (combined outcome of stroke progression or distinct recurrent stroke) within 90 days. Imaging parameters not included in the original CATCH imaging (CT/CTA and MRI) metrics were assessed for prediction of recurrent events. We also completed an exploratory analysis comparing predictors of symptom progression versus recurrence. Results: There were 36 recurrent events (36/510, 7.1% (95%CI: 5.0-9.6)) including 19 progression and 17 recurrent strokes. On CT/CTA: white matter disease, prior stroke, aortic arch focal plaque≥4mm, or intraluminal thrombus did not predict recurrent events. On MRI: white matter disease, prior stroke, and microbleeds did not predict recurrent events. The only additional clinical predictor was symptom fluctuation (hazard ratio 2.3; 95% CI: 1.05-5.0). Parameters predicting symptom progression included: ongoing symptoms at initial assessment, symptom fluctuation, intracranial occlusion, intracranial occlusion or stenosis, and the CT/CTA metric. No parameter was strongly predictive of recurrent stroke. Conclusions: There was no imaging parameter that could improve upon our original CT/CTA or MRI metrics to predict recurrent events after TIA and minor stroke. Only the addition of symptom fluctuation to the CT/CTA metric improved the prediction of recurrent events. Imaging was more predictive of symptom progression than distinct recurrent events.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".