Abstract 189: STAR: CT and MR Perfusion Imaging and Good Outcomes in Endovascular Stroke Treatment
Bibliographic record
Abstract
Background: Perfusion imaging may be used to select stroke patients for endovascular treatment, although few data are available on studies of CT and MRI, as used in Solitaire Flow Restoration Thrombectomy for Acute Revascularization (STAR), a prospective, multicenter, single-arm trial. Methods: The CT/MRI core lab processed available CT and MR perfusion source images with Olea Sphere 2.2 to measure volumes of infarct core and hypoperfusion. Established definitions for CT and MRI (core: rCBF < 31% (CT); ADC < 600х10 -6 mm 2 /s (MRI); hypoperfusion: Tmax > 6s (CT and MRI)) were used to provide equivalent measures. Core and hypoperfusion volumes, mismatch ratio and DEFUSE-2 categories of target and malignant profile were used to define the relationship of perfusion imaging with clinical outcomes. Results: 87 of 202 cases in STAR (mean age 68.7 ± 12.5 years; 57% women; median baseline NIHSS 15 (8-25)) had CT (60/87 or 69%) or MRI (27/87 or 31%) perfusion imaging processed. This cohort was similar to other STAR cases with mean time to treatment of 263 ± 95 min and IV tPA before thrombectomy in 55%. Infarct core volume was mean 11.2 ± 19.7 (0-116.9) cc with hypoperfusion volume of mean 70.6 ± 46.0 (0-182.2) cc. Mismatch ratio was mean 17.8 ± 53.7. 75/87 (86%) cases were categorized as target mismatch and only 5/87 (6%) as malignant profile. 58/87 patients (67%) had a mRS of 0, 1, or 2 at 90 days and 39/87 (45%) with a mRS of 0 or 1. Multivariate logistic regression identified that age (OR 0.91, p=0.010), time to treatment (OR 0.23, p=0.001), and Tmax > 10 s volume (OR 0.96, p=0.003) were associated with good outcomes (mRS 0-2 at 90 days). Only 2/87 (2.3%) patients had symptomatic ICH. Target mismatch exhibited a trend for good outcomes (p=0.149). Conclusions: STAR enrolled subjects with small infarct cores and extensive mismatch, achieving good clinical outcomes in a majority of cases treated with endovascular therapy. Randomized trials using CT and MR perfusion imaging are warranted to assess the impact of current endovascular stroke treatments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".