Abstract 128: TICI Reperfusion in HERMES: Success in Endovascular Stroke Therapy
Bibliographic record
Abstract
Background: Successful revascularization after endovascular therapy for acute ischemic stroke is measured by TICI score, yet variability exists in scale definitions and use. We examined the degree of reperfusion and association with outcomes in the HERMES collaboration of recent endovascular trials. Methods: An independent reader of the HERMES Imaging Core, blind to all other data, evaluated the angiography of subjects treated with endovascular therapy in HERMES. A battery of various TICI scores (mTICI, oTICI, oTICI2C) was used to define reperfusion of the initial target occlusion on noninvasive imaging (ITO) and conventional angiography (CATO). Statistical analyses examined all TICI reperfusion metrics and correlation with clinical outcomes. Results: Angiography of 593 subjects was analyzed, including ITO (124 ICA, 413 M1, 47 M2) and CATO (161 ICA, 329 M1, 62 M2). Across the entire scale range (0-3), the mTICI (AUC 0.61), oTICI (AUC 0.61) and oTICI2C (AUC 0.62) revealed similar ROC characteristics (p=0.450) in discriminating that more reperfusion is associated with better clinical outcomes. Using oTICI2C (3=100%, 2C=90-99%, o2B=67-89%, m2B=50-66%) of CATO, there were 44 TICI 3 (8%), 125 TICI 2C (22%), 178 TICI o2B (32%), 80 TICI m2B (14%), 85 TICI 2A (15%), 15 TICI 1 (3%) and 35 TICI 0 (6%). mRS shift analyses from baseline to 90 days revealed increasing TICI grades were linked with better outcomes (Figure), with significant distinctions of m2B vs. 2C (p=0.023) and all 2B combined vs. 3 (p=0.045). Conclusions: The benefit of endovascular therapy in HERMES was strongly associated with increasing degrees of TICI reperfusion. The oTICI2C metric reveals important distinctions in clinical outcomes that should be used in future studies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".