Magnetic Resonance Imaging versus Computed Tomography in Transient Ischemic Attack and Minor Stroke: The More Υou See the More You Know
Bibliographic record
Abstract
BACKGROUND: Magnetic resonance imaging (MRI) is proposed as the preferred imaging modality to investigate patients with transient ischemic attack (TIA). This is mainly based on a higher yield of small acute ischemic lesions; however, direct prospective comparisons are lacking. In this study, we aimed to directly compare the yield of acute ischemic lesions on MRI and computed tomography (CT) in the emergency diagnosis of suspected TIA or minor stroke. METHODS: Consecutive patients aged 18 years or older presenting with minor stroke (NIHSS <4) or high-risk TIA and who were examined by a stroke neurologist within 24 h of symptom onset were prospectively enrolled in the CATCH study. Patients who had undergone both a baseline CT and an MRI within 24 h of symptom onset were included in this substudy. Baseline MRI and CT were interpreted independently to identify an acute ischemic lesion. The rates of acute ischemic lesions on CT and MRI were compared, and the volume of acute ischemic lesions was measured on MRI. In addition, the volume of acute ischemic lesions on MRI was compared between patients who had evidence of acute ischemia on CT and in those who did not. RESULTS: A total of 347 patients were included, 168 with TIAs, 147 with minor strokes and 32 with a final diagnosis of a mimic. Acute ischemic lesions were detected in 39% of TIAs by using MRI versus 8% by using CT (p < 0.0001) and in 86% of minor strokes by using MRI versus 18% by using CT (p < 0.0001). Compared to MRI, CT had a sensitivity of 20% and a specificity of 98% in identifying an acute ischemic lesion. The infarct volume on diffusion-weighted MRI was larger in cases where the CT also showed an acute ischemic lesion (median 5.07 ml, IQR 10) as compared to lesions seen only on MRI (median 0.68 ml, IQR 1.31, p < 0.0001). CONCLUSION: MRI is superior to CT in detecting the small ischemic lesions occurring after TIA and minor stroke. Since these lesions are clinically relevant, MRI should be the preferred imaging modality in this setting.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".