Predictors and prognoses of Willisian collateral failure during mechanical thrombectomy
Bibliographic record
Abstract
During mechanical thrombectomy in the anterior cerebral circulation, thrombus embolization resulting in Willisian collateral failure may lead to critical stroke outcomes due to a shutdown of leptomeningeal collaterals. We hypothesized that the outcomes of dynamic Willisian collateral failure (DWF), induced during mechanical thrombectomy, would be associated with grave outcomes. We evaluated this hypothesis in consecutive patients, between January 2011 and May 2016, who underwent mechanical thrombectomy for anterior circulation occlusions, with an onset-to-puncture of 24 h. Patients with initial Willisian collateral failure (IWF) were identified first, with remaining patients classified into the DWF and Willisian collateral sparing (WCS) groups. Comparative and multivariable analyses were performed to predict grave outcomes (3-month modified Rankin Scale score of 5-6). Among 567 patients, 37 were in the IWF group, 38 in the DWF group, and 492 in the WCS group. Compared to the WCS and DWF groups, the IWF group had a higher baseline National Institute of Health Stroke Scale score and lower Alberta Stroke Program Early CT Score. The prevalence of grave outcomes was similarly high in the IWF (48.6%) and DWF (47.4%) groups, but lower in the WCS group (22.0%; p < 0.001). IWF and DWF were independent risk factors for a grave outcome.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".