Collaterals at angiography guide clinical outcomes after endovascular stroke therapy in HERMES
Bibliographic record
Abstract
BACKGROUND: Robust collateral circulation has been linked with better reperfusion and clinical outcomes. It remains unclear how individual assessments of collateral circulation may be translated into clinical practice. METHODS: The pooled Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) angiography dataset was analyzed by a centralized, independent imaging core blinded to other clinical data. Conventional angiography was acquired immediately prior to endovascular therapy. Collaterals were graded with the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) system and associated with baseline patient characteristics, reperfusion, and day 90 modified Rankin Score (mRS). Both 90-day all-cause mortality and day 90 mRS were modeled via multivariable logistic regression. RESULTS: Angiography was available in 376/605 (62%) patients. Baseline ASPECTS (Alberta Stroke Program Early CT Score) (p=0.043), history of diabetes mellitus (p=0.048), site of occlusion (p<0.001), and degree of subsequent Thrombolysis in Cerebral Infarction (TICI) reperfusion (p<0.001) were associated with collateral grades. ASITN collateral grade was strongly associated with ordinal mRS from baseline to 90 days in an unadjusted analysis (p<0.001). Multivariable regression demonstrated that collateral status is a strong determinant of mRS outcome in the presence of other predictors (OR=1.37 per grade, 95% CI [1.05 to 1.74], p=0.018). By comparing ORs, 1 unit of ASITN was determined to be approximately equivalent to 4.5 points of NIHSS, 11 years of age, 1.5 points of ASPECTS, or 100 min less delay from onset to puncture, in terms of impact on mRS. CONCLUSIONS: Individual collateral physiology may contribute significantly to reperfusion success and clinical outcomes after acute ischemic stroke. Building a consensus for the role of angiographic collateral assessment in the allocation of adjuvant reperfusion therapies may help galvanize a precision medicine approach in stroke.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".