Diffusion-Weighted Magnetic Resonance Imaging May Underestimate Acute Ischemic Lesions
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diffusion-weighted imaging sometimes fails to detect early computed tomography (CT) ischemic lesions in acute ischemic stroke patients, which is termed reversed discrepancy (RD), but its clinical significance remains unclear. The incidence and factors associated with RD in acute ischemic stroke patients within 3 hours of onset were examined. METHODS: A total of 164 consecutive patients with acute anterior circulation ischemic stroke was enrolled. All patients underwent both magnetic resonance imaging and CT within 3 hours of onset and before treatment. Their early ischemic changes were evaluated with the Alberta Stroke Program Early CT Score. RD was defined as present when the early ischemic change detected on CT was not seen on diffusion-weighted imaging. RESULTS: RD was found in 40 patients (24%). RD group patients were older (78.7 ± 9.6 versus 74.1 ± 12.1 years; P=0.03) and had a higher admission National Institutes of Health Stroke Scale score (median, 22 versus 11; P<0.01), higher rates of atrial fibrillation (75% versus 42%; P<0.01), a higher rate of internal carotid artery/middle cerebral artery proximal occlusion (55% versus 28%; P<0.01), and lower CT-Alberta Stroke Program Early CT Score (median 5 versus 10; P<0.01) and diffusion-weighted imaging-Alberta Stroke Program Early CT Score (7 versus 9; P<0.01) than patients in the non-RD group. Multivariate logistic regression analysis demonstrated that atrial fibrillation was independently associated with the presence of RD (odds ratio, 2.47; 95% CI, 1.05-6.12). CONCLUSIONS: RD is observed in a quarter of acute ischemic stroke patients. RD should be taken into consideration, especially in patients with atrial fibrillation, to prevent underestimating the extent of ischemic lesions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".