Abstract TP57: Predicting Regional Tissue Fate in Ischemic Stroke Using Color Coded mCTA-based Collaterals Assessment
Bibliographic record
Abstract
Background and Purpose: Multiphase CT angiography (mCTA) assessment of collaterals has been proven to be predictive of tissue fate on follow-up imaging following an acute stroke. We used the GE FastStroke Software, a novel mCTA image analysis and visualization tool that color codes pial arteries and veins based on delay in vessel enhancement to predict regional tissue fate. Red colored collaterals represented no delay in enhancement, green represented a one phase delay and blue two phase delays. Methods: Forty patients with M1-middle cerebral artery occlusions were selected from the prospective database ProVeIT. Baseline CT as well as 24 to 36-hour CT/MRI were analyzed and scored regionally using the Alberta Stroke Program early CT scoring (ASPECTS). Regional vessel filling was assessed using color coded collateral maps, and given a score of 1,2 or 3 based on the predominant color of collaterals (1=blue, 2=green, 3=red). A score of 0 was given if there was less than 50% regional filling compared to the contralateral side. Logistic regression method was used to correlate collateral assessment with tissue fate, adjusting for baseline ASPECTS. Results: 280 cortical regions were assessed (M1 to M6, Insula). When blue color was predominant, risk of subsequent infarction was 48% (27/56). Green and red colors were associated with 33% (37/111) and 14% (13/88) of subsequent infarction, respectively. A score of 0 had an 80% (20/25) risk of infarction. Using logistic regression methods, collaterals assessment met statistical significance in tissue fate correlation (p<0.001). Conclusions: Collateral scoring using color coded collateral maps was highly predictive of subsequent infarction risk. Further work is needed to understand the impact of fast endovascular reperfusion in influencing tissue outcome.
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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 | 0.002 |
| 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) | 0.002 | 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".