Cortical Venous Filling on Dynamic Computed Tomographic Angiography : A Novel Predictor of Clinical Outcome in Patients with Acute Middle Cerebral Artery Stroke
Why this work is in the frame
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Bibliographic record
Abstract
Background and Purpose-Venous flow in the downstream territory of an occluded artery may influence patient prognosis after ischemic stroke. Our aim was to study cortical venous filling (CVF) in a time-resolved manner with dynamic computed tomographic angiography and to assess the relationship with clinical outcome. Methods-Patients with a proximal middle cerebral artery occlusion underwent noncontrast CT and whole-brain CT perfusion/dynamic CT angiography within 9 hours after stroke-onset. We defined poor outcome as a modified Rankin Scale score of ≥3. Association between the extent and velocity of CVF and poor outcome at 3 months was analyzed with Poisson-regression. Prognostic value of optimal CVF (maximum opacification of cortical veins) in addition to age, stroke severity, treatment, Alberta Stroke Program Early CT score, cerebral blood flow, and collateral status was assessed with logistic regression and summarized with the area under the curve. Results-Eighty-eight patients were included, with a mean age of 67 years. By combining the extent and velocity of optimal CVF, we observed a decreased risk of poor outcome in patients with good and fast optimal CVF, risk ratio of 0.5 (95% confidence interval, 0.3-0.7). Extent and velocity of optimal CVF had additional prognostic value (area under the curve, 0.88; 95% confidence interval, 0.77-0.98; P
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it