Combined Multimodal Computed Tomography Score Correlates With Futile Recanalization After Thrombectomy in Patients With Acute Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Futile recanalization after acute ischemic stroke occurs in almost half of the patients despite optimal angiographic results. Multimodal neuroimaging may help to improve patient's selection but is still dismissed by many interventionalists. Our aim was to evaluate the accuracy of each parameter of multimodal computed tomography (CT) and their combination for predicting futile recanalization after successful thrombectomy. METHODS: We retrospectively reviewed a cohort of consecutive patients with anterior circulation stroke, fully assessable multimodal CT, and successful recanalization. Nonenhanced CT, CT angiography source images, cerebral blood volume (CBV), cerebral blood flow (CBF), and mismatch CBV-CBF maps were studied by Alberta Stroke Program Early CT Score (ASPECTS); collaterals on CT angiography were graded as poor or good (≤50% or >50% of the middle cerebral artery territory). Futile recanalization was defined as modified Rankin Scale score >2 at 3 months despite successful recanalization. RESULTS: One hundred fifty patients were included and 57% of them had futile recanalization. They had lower ASPECTS on nonenhanced CT, CT angiography source images, CBV, CBF, and mismatch CBV-CBF and presented more frequently poor collaterals (all P<0.001). Among them, CBV showed the highest area under the curve (0.83; 95% confidence interval, 0.76-0.88). In multivariate analyses, CT angiography source images ≤5 (odds ratio, 5.1; 95% confidence interval, 1.2-21.9), CBV≤6 (odds ratio, 3.5; 95% confidence interval, 1.2-9.7), and poor collaterals (odds ratio, 8.6; 95% confidence interval, 1.8-41.7) were independent predictors of futile recanalization. A combined score of these 3 parameters added complementary information: 57% of the patients with score-1, 89% with score-2, and 100% with score-3 had futile recanalization. Reclassification analyses indicated that this score improved prediction of futile recanalization. CONCLUSIONS: In this population, a combined multimodal CT score predicted futile recanalization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".