Acute Stroke Assessment with CT: Do We Need Multimodal Evaluation?
Bibliographic record
Abstract
PURPOSE: To assess detection of stroke and prediction of extent of infarction with multimodal computed tomographic (CT) evaluation (unenhanced CT, perfusion CT, and CT angiography) in patients suspected of having acute stroke. MATERIALS AND METHODS: Forty-four consecutive patients with a mean National Institutes of Health Stroke Scale score of 10.45 and suspected of having ischemic stroke of the anterior circulation were examined with multi-detector row CT within 8 hours (mean, 3.05 hours) of onset of symptoms. All evaluations were performed with the knowledge that acute stroke was suspected but without detailed clinical information. The extent of ischemia or final infarction on the baseline unenhanced CT scan and follow-up images was assessed with the Alberta Stroke Program Early CT score. Different perfusion maps and follow-up images were assessed to determine the percentage of the ischemia-affected hemisphere. Each component, as well as the multimodal CT evaluation, was compared with follow-up unenhanced CT scans or magnetic resonance images after a mean time of 2.32 days. RESULTS: Multimodal CT revealed true-positive findings in 30 of 41 patients and true-negative findings in three, resulting in a sensitivity of 78.9%. Unenhanced CT, CT angiography, and perfusion CT showed sensitivities of 55.3%, 57.9%, and 76.3%, respectively. In eight patients, small infarctions (mean size, 1.47 cm) that were proved at follow-up were missed with all modalities at initial multimodal CT. With perfusion CT, four of these small infarctions were missed within the white matter of the section levels. Maps of cerebral blood flow showed the best correlation with the final size of infarction with an r(2) value of 0.71. CONCLUSION: The presented multimodal CT evaluation improves detection rate and prediction of the final size of infarction in comparison with unenhanced CT, CT angiography, and perfusion CT alone.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".