A Combined Arterial and Venous Grading Scale to Predict Outcome in Anterior Circulation Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: Prognostic evaluation based on cortical vein score difference in stroke (PRECISE) score, a novel venous grading scale better predicted stroke outcomes. Henceforth, we aimed to describe and determine if a physiologically relevant combined arterial and venous grading scale (CRISP grading scale) is accurate in determining 90-day stroke outcomes in patients with proximal arterial occlusion in the anterior circulation. METHODS: Data are from the Keimyung Stroke Registry. Consecutive patients with M1 middle cerebral artery (MCA) or terminal internal carotid artery (ICA) occlusion on CT-angiography (CTA) from May-2004 to July-2008 were included. The affected hemisphere 'four veins composite score' and 'arterial collaterals' were each graded 'good' and 'poor'. On the combined scale, a 'good' grade represented a 'good' score on both scales and a 'poor' grade represented a 'poor' score on both scales. The 'other two' combinations were graded 'intermediate.' RESULTS: Eighty-one patients were included in the study. Dummy variable regression analysis demonstrated that poor outcome was commonly seen in the group with poor arterial and venous grades [OR(95%CI); 48 (8.24, 279.598); P < 0.00001] as opposed to poor arterial collaterals alone [OR(95%CI); 9.6(1.483,62.162); P = 0.018]. In multivariate analysis the CRISP grade [OR(95%CI); 2.638(1.192, 6.039), P = 0.017] and National Institutes of Health Stroke Scale [OR(95%CI);1.230(1.085, 1.395),P = 0.001(per unit increase)] emerged as the independent predictors of poor outcome (modified Rankin Scale >2) when adjusted for other imaging predictors of outcome. CONCLUSION: CRISP grading was precise in predicting stroke outcomes when compared to individual imaging scales including arterial collateral grading, PRECISE score and CTA-SI ASPECTS in patients with proximal arterial occlusion in the anterior circulation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".