Effects of reperfusion grade and reperfusion strategy on the clinical outcome: Insights from ESCAPE-NA1 trial
Bibliographic record
Abstract
BACKGROUND: We evaluated the association of reperfusion quality and different patterns of achieved reperfusion with clinical and radiological outcomes in the ESCAPE NA1 trial. METHODS: Data are from the ESCAPE-NA1 trial. Good clinical outcome [90-day modified Rankin Scale (mRS) 0-2], excellent outcome (90-day mRS0-1), isolated subarachnoid hemorrhage, symptomatic hemorrhage (sICH) on follow-up imaging, and death were compared across different levels of reperfusion defined by expanded Treatment in Cerebral Infarction (eTICI) Scale. Comparisons were also made between patients with (a) first-pass eTICI 2c3 reperfusion vs multiple-pass eTICI 2c3; (b) final eTICI 2b reperfusion vs eTICI 2b converted-to-eTICI 2c3; (c) sudden reperfusion vs gradual reperfusion if >1 pass was required. Multivariable logistic regression was used to test associations of reperfusion grade and clinical outcomes. RESULTS: Of 1037 included patients, final eTICI 0-1 was achieved in 46 (4.4%), eTICI 2a in 76 (7.3%), eTICI 2b in 424 (40.9%), eTICI 2c in 284 (27.4%), and eTICI 3 in 207 (20%) patients. The odds for good and excellent clinical outcome gradually increased with improved reperfusion grades (adjOR ranging from 5.7-29.3 and 4.3-17.6) and decreased for sICH and death. No differences in outcomes between first-pass versus multiple-pass eTICI 2c3, eTICI 2b converted-to-eTICI 2c3 versus unchanged eTICI 2b and between sudden versus gradual eTICI 2c3 reperfusion were observed. CONCLUSION: Better reperfusion degrees significantly improved clinical outcomes and reduced mortality, independent of the number of passes and whether eTICI 2c3 was achieved suddenly or gradually.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".