Computed Tomographic Angiography and Cerebral Blood Volume Can Predict Final Infarct Volume and Outcome After Recanalization
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recanalization rates are higher in acute anterior stroke treated with stent-retrievers when compared with older techniques. However, some still have sizeable infarcts and poor outcome. This may be related to underestimation of core infarct on nonenhanced computed tomography (NECT). CT angiography (CTA) source images (CTASI) and CT perfusion may be more informative. We hypothesize that core infarct estimation with NECT, CTA, and CT perfusion predicts infarct at 24 hours and outcome after fast recanalization. METHODS: Consecutive good recanalization patients with proximal anterior circulation stroke were evaluated. We assessed Alberta Stroke Program Early CT Score (ASPECTs) on NECT for subtle early infarct, hypodensity, loss of gray-white (CTASI), and low cerebral blood volume (CBV; CT perfusion). Sensitivity and specificity for predicting infarct by region were calculated. RESULTS: Of 46 patients, 36 (78%) had successful thrombectomy. Median ASPECTS was 10 for NECT early infarct and frank hypodensity; for CBV, CTASI-ASPECTS was 8. CTASI had the highest sensitivity of 71% and specificity of 82% for 24 hours NECT infarct. There was moderate correlation and concordance between CBV/24-hour NECT (Rp=0.51; Rc=0.50) and CTASI/24-hour NECT (Rp=0.54 and Rc=0.53). Thirty-four patients (74%) had good outcomes. Median ASPECTS was higher on CTASI (8 versus 5; P=0.04) and CBV (9 versus 5; P=0.03) for patients with good versus bad outcome. There were better outcomes with increasing CTASI-ASPECTS (P=0.004) and CBV-ASPECTS (P=0.02). CONCLUSIONS: CTASI and CBV were better at predicting 24-hour infarct and outcome than NECT. Appropriate advanced imaged guided selection may improve outcomes in large-vessel stroke treated with the newest techniques.
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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 | 0.000 | 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) | 0.002 | 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".