Collateral Scoring on CT Angiogram Must Evaluate Phase and Regional Pattern
Bibliographic record
Abstract
PURPOSE: We measured anterior cerebral artery (ACA)-middle cerebral artery (MCA) and posterior cerebral artery (PCA)-MCA pial filling on single-phase computed tomography angiograms (sCTAs) in acute ischemic stroke and correlate with the CTA-based Massachusetts General Hospital (MGH) and digital subtraction angiography (DSA)-based American Society of Interventional and Therapeutic Neuroradiology (ASITN) score. METHODS: Patients with acute stroke and M1 MCA±intracranial internal carotid artery occlusion on baseline CTA were included. Baseline sCTA was assessed for phase of image acquisition. An evaluator assessed collaterals using the Calgary Collateral (CC) Score (measures pial arterial filling in ACA-MCA and PCA-MCA regions separately), the CTA-based MGH score, and on DSA using the ASITN score. Infarct volumes were measured on 24- to 48-hour magnetic resonance imaging/ computed tomography. RESULTS: Of 106 patients, baseline sCTA was acquired in early arterial phase in 9.9%, peak arterial in 50.7%, equilibrium in 32.4%, early venous in 5.6%, and late venous in 1.4%. Variance in ACA-MCA collaterals explained only 32% of variance in PCA-MCA collaterals on the CC score (Spearman's correlation coefficient rho [rho]=0.56). Correlation between ACA-MCA collaterals and the MGH score was strong (rho=0.8); correlation between PCA-MCA collaterals and this score was modest (rho=0.54). Correlation between ACA-MCA collaterals and the ASITN score was modest (n=53, rho=0.43); and correlation between PCA-MCA collaterals and ASITN score was poor (rho=0.33). Of the CTA-based scores, the CC Score (Akaike [AIC] 1022) was better at predicting follow-up infarct volumes than was the MGH score (AIC 1029). CONCLUSION: Collateral assessments in acute ischemic stroke are best done using CTA with temporal resolution and by assessing regional variability. ACA-MCA and MCA-PCA collaterals should be evaluated separately.
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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 | 0.004 |
| 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".