Abstract 176: HERMES: Collaterals at Angiography Guide Clinical Outcomes
Bibliographic record
Abstract
Background: Collateral circulation is a key factor in the pathophysiology of ischemic stroke. We conducted detailed analyses of angiography acquired immediately prior to endovascular therapy in the HERMES collaboration of recent landmark thrombectomy trials to determine predictors of collateral status and assess impact on clinical outcomes. Methods: The HERMES Imaging Core, blind to all other clinical and imaging data, independently interpreted conventional angiography acquired immediately prior to endovascular therapy. Collaterals were graded with the ASITN scale, based on available data for the site of arterial occlusion defined on initial injections. The statistical core analyzed the association of collateral grade with demographics, baseline NIHSS, site of arterial occlusion and clinical outcomes of day 90 mRS. Results: Angiography of collaterals was available in 376/605 (62%), including ASITN grades 0 in 7 (2%), 1 in 40 (10%), 2 in 182 (48%), 3 in 129 (34%) and 4 in 18 (5%). Elevated blood glucose (p=0.011) and diabetes (p=0.048) were associated with worse collateral grades, but age and NIHSS were unrelated. Better collateral grade was strongly associated with the degree of subsequent TICI reperfusion (p<0.001). The limited numbers of symptomatic intracranial hemorrhage (14/376 or 3.7%) or parenchymal hematomas (18/376 or 4.8%) precluded analysis of association with collateral status. Collaterals had strong impact on mRS shift from baseline to 90 days (p<0.001). Multivariable regression revealed that better collateral status was a potent determinant (OR 1.37 per grade, p=0.028) of outcomes, equivalent to 4.5 points of NIHSS and 14 years of age in terms of impact on mRS outcomes. Conclusions: Collaterals at angiography were a potent determinant of clinical outcomes in recent landmark thrombectomy trials.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".