Acute Perfusion and Diffusion Abnormalities Predict Early New MRI Lesions 1 Week After Minor Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Transient ischemic attack and minor stroke are associated with high ischemic recurrence in the first week. We prospectively studied the correlation between baseline diffusion/perfusion deficits and development of new ischemic lesions. METHODS: Patients with transient ischemic attack and those with minor stroke (n=50) underwent MRI at admission. Acute perfusion-weighted imaging deficit (Tmax+2-second delay) and diffusion-weighted imaging (DWI) lesion volumes were measured planimetrically. Follow-up scans were examined for new DWI/fluid-attenuated inversion recovery lesions at Days 7 and 30. RESULTS: Twenty-eight patients (56%) had acute DWI lesions. New DWI lesions developed in 9 of 50 patients (18%) at 1 week and 11 of 50 (cumulative 22%) at 4 weeks. Patients with new infarcts were more likely to have baseline DWI lesions (χ²=8.264, P=0.003). Baseline DWI lesion volume was significantly larger in those who developed new lesions at Day 7 (median, 13.2 mL; interquartile range, 12 versus median 0.1 mL; interquartile range, 2 mL; P<0.001) and Day 30 (11.1 mL; interquartile range, 13 mL versus 0.1 mL; interquartile range, 13 mL; P<0.001). Thirty-eight patients had baseline perfusion-weighted imaging. Patients with recurrent lesions were more likely to have baseline perfusion deficits (χ²=19.5, P<0.0001). All new lesions developed within the baseline hypoperfused regions. Baseline DWI lesion volume predicted new lesion development at day 7 (OR, 1.17 per mL; CI, 1.05 to 1.30; P=0.005) and Day 30 (OR, 1.39 per mL; CI, 1.03 to 1.26; P=0.009) by regression analysis. CONCLUSIONS: Early recurrence of stroke is much more likely in patients with larger baseline DWI and perfusion-weighted imaging lesions. MRI lesion "recurrence" appears to be related to completion of the natural history of the original cerebrovascular syndrome rather than de novo events in most patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".