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 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 | 0.008 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".