Comparison of Three Scores of Collateral Status for Their Association With Clinical Outcome: The HERMES Collaboration
Bibliographic record
Abstract
Background: Leptomeningeal collateral status on baseline computed tomographic angiography (CTA) is associated with clinical outcome after acute ischemic stroke treatment. However, assessment of collateral status is not uniform. To compare 3 different CTA collateral scores (CS) and imaging techniques about their association with clinical outcome. Methods: Pooled analysis of patient-level data from the Highly Effective Reperfusion Using Multiple Endovascular Devices collaboration. Patients with large vessel occlusion from 7 randomized controlled trials that compared endovascular thrombectomy with standard medical care were included. Three different CS (Tan CS, regional CS [rCS], and regional Alberta Stroke Program Early CT Score CS) and 2 imaging techniques (single-phase [sCTA] and multiphase/dynamic CTA) were evaluated. Functional independence (modified Rankin Scale score 0–2) at 3 months poststroke was the primary outcome. Furthermore, we assessed the effect of sCTA image acquisition time on collateral status assessment using an adjusted ordinal logistic regression model to obtain predicted values for the trichotomized rCS. Results: Among 1147 pooled patients, 948 (82.7%) had sCTA and 199 (17.3%) multiphase/dynamic CTA as baseline angiography. With all 3 collateral scales, better CSs were associated with better 3-month functional outcome. With sCTA images, the rCS (area under the curve [AUC] 0.63) and regional Alberta Stroke Program Early CT Score CS (AUC 0.62) better predicted functional outcome than the Tan CS (AUC 0.60, respectively; P <0.001 and P =0.02). With multiphase/dynamic CTA images, all collateral scales performed similarly in predicting functional outcome (rCS [AUC 0.61]; regional Alberta Stroke Program Early CT Score CS [AUC 0.61] versus Tan CS [AUC 0.61], respectively; P =0.93 and P =0.91). Overall, no endovascular thrombectomy treatment effect modification by collateral status (rCS) was demonstrated ( P =0.41). sCTA timing independently influenced CS assessment. On earlier timed sCTA, the predicted proportions of scans with poor collaterals was higher and vice versa. Conclusions: In this data set of highly selected patients with stroke, using a regional CS on sCTA likely allows for the most accurate prediction of functional outcome while on time-resolved CTA, the type of CS did not matter. Patients across all collateral grades benefit from endovascular thrombectomy. sCTA timing independently influenced CS assessment.
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".