CT Perfusion Does Not Modify the Effect of Reperfusion in Patients with Acute Ischemic Stroke Undergoing Endovascular Treatment in the ESCAPE-NA1 Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although reperfusion is associated with improved outcomes in patients with acute ischemic stroke undergoing endovascular treatment, many patients still do poorly. We investigated whether CTP modifies the effect of near-complete reperfusion on clinical outcomes, ie, whether poor clinical outcomes despite near-complete reperfusion can be partly or fully explained by CTP findings. MATERIALS AND METHODS: Data are from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial. Admission CTP was processed using RAPID software, generating relative CBF and CBV volume maps at standard thresholds. CTP lesion volumes were compared in patients with-versus-without near-complete reperfusion. Associations between each CTP metric and clinical outcome (90-day mRS) were tested using multivariable logistic regression, adjusted for baseline imaging and clinical variables. Treatment-effect modification was assessed by introducing CTP lesion volume × reperfusion interaction terms in the models. RESULTS: CTP lesion volumes and reperfusion status were available in 410/1105 patients. CTP lesion volumes were overall larger in patients without near-complete reperfusion, albeit not always statistically significant. Increased CBF <34%, CBV <34%, CBV <38%, and CBV <42% lesion volumes were associated with worse clinical outcome (ordinal mRS) at 90 days. CTP core lesion volumes did not modify the treatment effect of near-complete recanalization on clinical outcome. CONCLUSIONS: CTP did not modify the effect of near-complete reperfusion on clinical outcomes. Thus, CTP cannot explain why some patients with near-complete reperfusion have poor clinical outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".