Perfusion MR Predicts Outcome in High-Risk Transient Ischemic Attack/Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transient or minor ischemic stroke is associated with an early risk of deterioration. Baseline perfusion-diffusion mismatch may predict clinical deterioration and infarct growth in this population. METHODS: High-risk transient ischemic attack and minor stroke (National Institutes of Health Stroke Scale ≤3) subjects were prospectively enrolled and imaged with MRI within 24 hours of symptom onset as part of sequential derivation and validation cohorts. Baseline diffusion-weighted imaging, perfusion-weighted imaging (Tmax≥4 s), mismatch (Tmax≥4 s-diffusion-weighted imaging), and follow-up fluid-attenuated inversion recovery infarct volumes were measured. Primary outcome was infarct growth on fluid-attenuated inversion recovery, and secondary outcome was symptom progression. RESULTS: One hundred thirty-seven and 281 subjects were included in the derivation and validation cohorts, respectively. Infarct growth occurred in 18.5% of the derivation and 5.5% of the validation cohorts. Symptom progression occurred in 9.5% of the derivation and 4.5% of the validation cohorts. In the derivation cohort, subjects with baseline mismatch were significantly more likely to show infarct growth on fluid-attenuated inversion recovery (relative risk [RR], 13.5; 95% confidence interval [CI], 4.2-38.9) and symptom progression (RR, 7.0; 95% CI, 2.0-7.3). A baseline mismatch volume of 10 mL in the derivation cohort was the optimal threshold to predict infarct growth (area under the curve, 0.89; 95% CI, 0.80-0.98). This threshold was highly predictive of infarct growth in the validation cohort (P=0.001). Baseline mismatch was associated with clinical deterioration in the derivation (area under the curve, 0.81; 95% CI, 0.67-0.96) and validation cohorts (area under the curve, 0.66; 95% CI, 0.46-0.85). CONCLUSIONS: Among subjects with high-risk transient ischemic attack and minor stroke, diffusion-weighted imaging-perfusion-weighted imaging mismatch predicts infarct growth and clinical deterioration. These findings suggest that reperfusion strategies would be beneficial in this population.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".