Refinement of Imaging Predictors of Recurrent Events following Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND: TIA and minor stroke have a high risk of recurrent stroke. Abnormalities on CT/CTA and MRI predict recurrent events in TIA and minor stroke. However there are many other imaging abnormalities that could potentially predict outcome that have not been assessed in this population. Also the definition of recurrent events used includes deterioration due to stroke progression or recurrent stroke and whether imaging is either of these is not known. AIMS: To improve upon the clinical, CT/CTA and MRI parameters that predict recurrent events after TIA and minor stroke by assessing further imaging parameters. Secondary aim was to explore predictors of stroke progression versus recurrent stroke. METHODS: 510 consecutive TIA and minor stroke patients had CT/CTA and most had MRI. Primary outcome was recurrent events (stroke progression or recurrent stroke) within 90 days. Further imaging parameters were assessed for prediction of recurrent events (combined outcome of stroke progression and recurrent stroke). We also explored predictors of symptom progression versus recurrence individually. RESULTS: 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≥4 mm, or intraluminal thrombus did not predict recurrent events (progression or recurrent stroke). On MRI: white matter disease, prior stroke, and microbleeds did not predict recurrent events. Parameters predicting the individual outcome of 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 a distinct recurrent stroke. CONCLUSIONS: There was no imaging parameter that could improve upon our original CT/CTA or MRI metrics to predict the combined outcome of stroke progression or a recurrent stroke after TIA and minor stroke. We are better at using imaging to predict stroke progression rather than recurrent 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".