Dynamic CT but Not Optimized Multiphase CT Angiography Accurately Identifies CT Perfusion Target Mismatch Ischemic Stroke Patients
Bibliographic record
Abstract
Imaging protocols for acute ischemic stroke varies significantly from center to center leading to challenges in research translation. We aimed to assess the inter-rater reliability of collateral grading systems derived from dynamic computed tomography angiography (CTA) and an optimized multiphase CTA and, to analyze the association of the two CTA modalities with CT perfusion (CTP) compartments by comparing the accuracy of dynamic CTA (dCTA) and optimized multiphase CTA (omCTA) in identifying CT perfusion (CTP) target mismatch patients. Acute ischemic stroke patients with a proximal large vessel occlusion who underwent whole brain CTP were included in the study. Collateral status were assessed using ASPECTS collaterals (Alberta Stroke Program Early CT Score on Collaterals) and ASITN/SIR collateral system (the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology) on dCTA and omCTA. Eighty-one patients were assessed, with a median ischemic core volume of 29 mL. The collateral assessment with ASPECTS collaterals using dCTA have a similar inter-rater agreement (K-alpha: 0.71) compared to omCTA (K-alpha: 0.69). However, the agreement between dCTA and CTP in classifying patients with target mismatch was higher compared to omCTA (Kappa, dCTA: 0.81; omCTA: 0.64). We found dCTA was more accurate than omCTA in identifying target mismatch patients with proximal large vessel occlusion.
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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 | 0.002 | 0.008 |
| 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 | 0.001 | 0.001 |
| 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".