Conventional Enhancement CT: A Valuable Tool for Evaluating Pial Collateral Flow in Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: To establish an easy and rapid method for evaluating pial collateral flow, we compared the Alberta Stroke Program Early CT Score (ASPECTS) on nonenhanced CT (NECT), conventional contrast-enhanced CT (CECT), and CT angiography source images (CTA-SI) in patients with acute ischemic stroke. METHODS: We reviewed 55 consecutive patients with acute ischemic stroke involving the anterior circulation who underwent thrombolysis within 6 h of referral to the stroke center. We evaluated axial images using NECT, CECT and CTA-SI. Pial collateral formation was graded as fair (1-2 points) or bad (3-5 points) based on 4-vessel angiography. The outcomes were dichotomized into good (modified Rankin Scale, mRS 0-2) or poor (mRS 3-6) using a 90-day mRS. RESULTS: Demographics (age, sex, initial National Institutes of Health Stroke Scale score, time to CT acquisition and stroke subtypes) did not significantly differ between patients with fair or bad collateral formation. ASPECTS on CECT (r = -0.788, p < 0.0001) was more inversely correlated with pial collateral formation than ASPECTS on NECT (r = -0.557, p < 0.0001) or ASPECTS on CTA-SI (r = -0.662, p < 0.0001). Furthermore, ASPECTS on CECT demonstrated a high discriminative capability, with an area under the receiver operating characteristic curve of 0.885 for fair collateral circulation, compared to 0.790 for ASPECTS on NECT and 0.794 for ASPECTS on CTA-SI. Multiple regression analysis revealed that ASPECTS on CECT (≥8) was an independent predictor for fair collateral circulation (odds ratio = 23.00, p < 0.001) and a good prognosis (odds ratio = 17.81, p < 0.001). CONCLUSION: ASPECTS on CECT is a feasible method for predicting pial collateral flow and overall outcomes in acute ischemic stroke.
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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".