Abstract 256: Proposal of multimodal CT‐based scoring system in prediction of hemorrhagic transformation in acute ischemic stroke
Bibliographic record
Abstract
Introduction The routinely used computed tomography (CT)‐based workup in the setting of acute ischemic stroke (AIS) includes non‐contrast brain CT, CT angiography (CTA), and CT perfusion. Several CT, CTA, CTP‐based radiological biomarkers of hemorrhagic transformation (HT) were reported. To assess the predictive value of the combined multimodal CT parameters for HT after AIS and proposal of predictive scoring scale. Methods The source images of the NCCT, CTA and CTP of 282 AIS patients involving the anterior circulation (HT = 91, non‐HT = 191) were retrospectively reviewed and the following biomarkers were recorded and analyzed: Early subtle ischemic signs, hyperdense middle cerebral artery sign (HMCAS) and Alberta Stroke Program Early CT Score (ASPECTS) < 7 in NCCT, large‐vessel occlusion (LVO), clot burden score (CBS) < 6, large‐vessel occlusion, poor collateral score (CS) and Tmax > 6 s ≥ 56.5 ml. A scoring system to predict HT based on these biomarkers was developed. Each biomarker counts for a single point with the total score ranging from 0 to 7. Results All the aforementioned multimodal CT biomarkers and the selected cut offs were significantly associated with higher HT risk. The calculated scores were statistically significant different between the HT and the non‐HT groups with AUC 0.761 (95% CI 0.703–0.819, P < 0.0000001). Rates of HT were approximately five times higher in patients with score ≥ 3. Conclusion Multimodal CT‐based scoring system may provide highly reliable predictive model of hemorrhagic transformation in acute ischemic stroke.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".