Abstract 38: Brain CT Perfusion is Superior to Non-contrast CT Aspects in the Evaluation of Hyperacute Infarct Volume
Bibliographic record
Abstract
Objective: To compare the predictive capacity to detect established infarct in acute anterior circulation stroke between the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) on non-contrast computed tomography (CT) and CT perfusion. Methods: Fifty-nine acute anterior circulation ischemic stroke patients received brain non-contrast CT, CT perfusion and hyperacute magnetic resonance imaging (MRI) within 100 minutes from CT imaging. ASPECTS scores were calculated by 4 independent vascular neurologists, blinded from CT perfusion and MRI data. CT perfusion infarct core volumes were calculated by MIStar software. The accuracy of commonly used ASPECTS cut-off scores and a CT perfusion core volume of ≥ 70 mL to detect a hyperacute MRI diffusion lesion of ≥ 70 ml was evaluated. Results: Median ASPECTS score was 9 (IQR 7-10). Median CT perfusion core volume was 22 ml (IQR 10.4-71.9). Median MRI diffusion lesion volume was 24,5 ml (IQR 10-63.9). ASPECTS score of < 6 had a sensitivity of 0.37, specificity of 0.95 and c-statistic of 0.66 to predict an acute MRI lesion ≥ 70 ml. In comparison, a CT perfusion core lesion of ≥ 70 ml had a sensitivity of 0.76, specificity of 0.98 and c-statistic of 0.92. The CT perfusion core lesion covered a median of 100% of the acute MRI lesion volume (IQR 86-100%). Conclusions: CT perfusion is superior to ASPECTS to predict hyperacute MRI lesion volume in ischemic stroke.
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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 | 0.008 |
| 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) | 0.003 | 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".