2 Imaging triage of late window patients with acute ischemic stroke. A comparative study using multi-phase CT angiography vs CT perfusion
Bibliographic record
Abstract
Background Current guidelines recommend the use of perfusion imaging for selection of patients for endovascular thrombectomy (EVT) beyond six hours from onset. The role of collateral imaging in this time window is not established. Methods We used data from a prospective multi-center observational study where all stroke patients with suspected large vessel occlusion underwent imaging with single- and multi-phase CT angiography (mCTA) as well as CT perfusion. For this analysis, we only included patients presenting beyond six hours from onset/last known well time. Two blinded reviewers judged patients’ eligibility for EVT using published collateral imaging (mCTA), compared to CT perfusion (using DAWN and DEFUSE-3 trials) selection criteria. All perfusion images were processed using an automated commercial software. The outcomes of patients eligible for EVT using mCTA, DAWN, or DEFUSE-3 criteria were compared using multivariable logistic regression modeling. Model predictive characteristics were assessed using c-statistic for the receiver operating curve, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Results Of 614 patients, 86 patients presented beyond six hours from onset/last known well (median 9.6 hours, IQR 4.1 hours). Median age was 71 years (IQR 14 years), 48.8% were females, median baseline NIHSS was 12 (IQR=11). Thirty-five patients (40.7%) received EVT of which good functional outcome (90 day modified Rankin scale 0–2) was achieved in 47%. Collateral-based imaging paradigms significantly modified the treatment effect of EVT on clinical outcome i.e. 90-day mRS 0–2 (P interaction=0.007). The mCTA-based regression model best fit the data for 90-day outcome (C statistic 0.86, 95% CI 0.77 to 0.94) and was associated with least information loss (AIC 95.7, BIC 114.9) when compared to CTP based models. Perfusion imaging paradigm using DEFUSE-3 criteria had better predictive properties than the DAWN trial criteria. Conclusion Collateral-based imaging paradigm using mCTA compares well with CTP in selecting patients for EVT in the late time window. Disclosures M. Almekhlafi: None. W. Kunz: None. R. McTaggart: None. M. Jayaraman: None. M. Najm: None. S. Ahn: None. E. Fainardi: None. M. Rubiera: None. A. Khaw: None. A. Zini: None. M. Hill: None. A. Demchuk: None. M. Goyal: None. B. Menon: None.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".