Early Magnetic Resonance Imaging in Transient Ischemic Attack and Minor Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The use of magnetic resonance imaging (MRI) after transient ischemic attack (TIA) or minor stroke may be affected by the relative timing of imaging. We measured the impact of scanning an individual patient late versus early after TIA and minor stroke. METHODS: Two hundred sixty-three TIA or minor stroke (National Institute of Health Stroke Scale score ≤3) patients with a baseline MRI completed within 24 hours of symptom onset and a follow-up MRI at 90 days were included. Baseline and 90-day scans were assessed independently for the presence of any stroke lesions that could explain the presenting symptoms. The presence and pattern of any stroke lesions were compared at the 2 time points. RESULTS: The presence of a stroke (acute or chronic) in any location was more common on baseline MRI versus 90-day MRI (68% vs 56%; P=0.005). Thirty percent of subjects with negative scans at 90 days had a clearly identifiable stroke at baseline. When interpreted blinded to the baseline scan, the presumed relevant lesion on the 90-day MR scan was the correct lesion in only 53% patients. One-third (34%) of patients had a different lesion pattern on the baseline scan compared with the 90-day scan. Ninety percent (80/89) of these patients had more lesions on the baseline MRI and 10% (9/89) had new lesions on the 90-day MRI. CONCLUSIONS: Delayed MRI after TIA or minor stroke reduces the diagnostic yield and results in missed understanding of the lesion pattern. MRI of minor stroke and TIA patients should occur early after symptom onset, and delayed imaging should be interpreted with caution.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".