Assessment of Intracranial Collaterals on CT Angiography in Anterior Circulation Acute Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Intracranial collaterals influence the prognosis of patients treated with intravenous tissue plasminogen activator in acute anterior circulation ischemic stroke. We compared the methods of scoring collaterals on pre-tPA brain CT angiography for predicting functional outcomes in acute anterior circulation ischemic stroke. MATERIALS AND METHODS: Two hundred consecutive patients with acute anterior circulation ischemic stroke treated with IV-tPA during 2010-2012 were included. Two independent neuroradiologists evaluated intracranial collaterals by using the Miteff system, Maas system, the modified Tan scale, and the Alberta Stroke Program Early CT Score 20-point methodology. Good and extremely poor outcomes at 3 months were defined by modified Rankin Scale scores of 0-1 and 5-6 points, respectively. RESULTS: Factors associated with good outcome on univariable analysis were younger age, female sex, hypertension, diabetes mellitus, atrial fibrillation, small infarct core (ASPECTS ≥8), vessel recanalization, lower pre-tPA NIHSS scores, and good collaterals according to Tan methodology, ASPECTS methodology, and Miteff methodology. On multivariable logistic regression, only lower NIHSS scores (OR, 1.186 per point; 95% CI, 1.079-1.302; P = .001), recanalization (OR, 5.599; 95% CI, 1.560-20.010; P = .008), and good collaterals by the Miteff method (OR, 3.341; 95% CI, 1.203-5.099; P = .014) were independent predictors of good outcome. Poor collaterals by the Miteff system (OR, 2.592; 95% CI, 1.113-6.038; P = .027), Maas system (OR, 2.580; 95% CI, 1.075-6.187; P = .034), and ASPECTS method ≤5 points (OR, 2.685; 95% CI, 1.156-6.237; P = .022) were independent predictors of extremely poor outcomes. CONCLUSIONS: Only the Miteff scoring system for intracranial collaterals is reliable for predicting favorable outcome in thrombolyzed acute anterior circulation ischemic stroke. However, poor outcomes can be predicted by most of the existing methods of scoring intracranial collaterals.
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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) | 0.001 | 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".