Diffusion-Weighted Imaging-Negative Patients With Transient Ischemic Attack Are at Risk of Recurrent Transient Events
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Among patients presenting with a transient ischemic attack (TIA), some clinical features predispose to recurrent TIA, whereas others predispose to subsequent strokes. We assessed the implication of negative diffusion-weighted imaging on a baseline MRI in predicting subsequent TIA. METHODS: We prospectively studied patients presenting in the emergency department within 12 hours of a TIA (motor or speech). All patients had a MRI within 24 hours of the index event. The primary outcome was TIA within 1 year of study entry. The 1-year risk of stroke was also evaluated. RESULTS: A total of 85 patients had a MRI, among which 35 patients (41.2%) had a diffusion-weighted imaging lesion. The mean time from symptom onset to MRI was 12.1 hours. Patients without a diffusion-weighted imaging lesion on baseline MRI were 4.6 times (27.4% versus 5.9%; P<0.05) more likely to have a subsequent TIA at 1 year than patients with a diffusion-weighted imaging lesion, but 4.3 times (2.1% versus 9.1%; P=0.19) less likely to have a subsequent stroke. CONCLUSIONS: The absence of a diffusion-weighted imaging lesion on the baseline scan predicts recurrent transient events rather than 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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".