Triaging transient ischemic attack and minor stroke patients using acute magnetic resonance imaging
Bibliographic record
Abstract
We examined whether the presence of diffusion-weighted imaging (DWI) lesions and vessel occlusion on acute brain magnetic resonance images of minor stroke and transient ischemic attack patients predicted the occurrence of subsequent stroke and functional outcome. 120 transient ischemic attack or minor stroke (National Institutes of Health Stroke Scale < or = 3) patients were prospectively enrolled. All were examined within 12 hours and had a magnetic resonance scan within 24 hours. Overall, the 90-day risk for recurrent stroke was 11.7%. Patients with a DWI lesion were at greater risk for having a subsequent stroke than patients without and risk was greatest in the presence of vessel occlusion and a DWI lesion. The 90-day risk rates, adjusted for baseline characteristics, were 4.3% (no DWI lesion), 10.8% (DWI lesion but no vessel occlusion), and 32.6% (DWI lesion and vessel occlusion) (p = 0.02). The percentages of patients who were functionally dependent at 90 days in the three groups were 1.9%, 6.2%, and 21.0%, respectively (p = 0.04). The presence of a DWI lesion and a vessel occlusion on a magnetic resonance image among patients presenting acutely with a transient ischemic attack or minor stroke is predictive of an increased risk for future stroke and functional dependence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".