Cortical Venous Filling on Dynamic Computed Tomographic Angiography
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<0.02) compared with a model without CVF information. CONCLUSIONS: The combination of extent and velocity of optimal CVF, as assessed with dynamic CT angiography, is useful to identify patients with acute middle cerebral artery stroke at higher risk of poor clinical outcome at 3-month follow-up. CLINICAL TRIAL REGISTRATION: URL: http://www.trialregister.nl/trialreg and http://www.clinicaltrials.gov. Unique identifier: NTR1804 and NCT00880113, respectively.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".