Imaging Triage of Patients with Late-Window (6–24 Hours) Acute Ischemic Stroke: A Comparative Study Using Multiphase CT Angiography versus CT Perfusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The role of collateral imaging in selecting patients for endovascular thrombectomy beyond 6 hours from onset has not been established. To assess the comparative utility of collateral imaging using multiphase CTA in selecting late window patients for EVT. MATERIALS AND METHODS: We used data from a prospective multicenter observational study in which all patients underwent imaging with multiphase CT angiography as well as CTP. Two blinded reviewers evaluated patients’ eligibility for endovascular thrombectomy using published collateral imaging (multiphase CTA) criteria compared with CTP using the selection criteria of the Clinical Mismatch in the Triage of Wake Up and Late Presenting Strokes Undergoing Neurointervention with Trevo (DAWN) and Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3 (DEFUSE-3) trials. CTP images were processed using automated commercial software. The outcomes of patients eligible for endovascular thrombectomy according to multiphase CTA, DAWN, or DEFUSE-3 criteria were compared using multivariable logistic regression modeling. Model characteristics were compared using the C-statistic for the receiver operating characteristic curve, the Akaike information criterion, and the Bayesian information criterion. RESULTS: Eighty-six patients presented beyond 6 hours from onset/last known well (median, 9.6 hours; interquartile range, 4.1 hours). Thirty-five patients (40.7%) received endovascular thrombectomy, of whom good functional outcome (90-day mRS, 0–2) was achieved in 16/35 (47%). Collateral-based imaging paradigms significantly modified the treatment effect of endovascular thrombectomy on 90-day mRS 0–2 (Pinteraction = .007). The multiphase CTA–based regression model best fit the data for the 90-day outcome (C-statistic, 0.86; 95% CI, 0.77–0.94) and was associated with the least information loss (Akaike information criterion, 95.7; Bayesian information criterion, 114.9) compared with CTP-based models. CONCLUSIONS: The collateral-based imaging paradigm using multiphase CTA compares well with CTP in selecting patients for endovascular thrombectomy in the late time window.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".