Does Small Vessel Disease Burden Impact Collateral Circulation in Ischemic Stroke Treated by Mechanical Thrombectomy?
Bibliographic record
Abstract
Background and Purpose- The development of leptomeningeal collateral artery network might be adversely affected by small vessel wall alteration. We sought to determine whether small vessel disease (SVD) burden may impact collateral development in patients treated by mechanical thrombectomy for anterior circulation acute ischemic stroke. Methods- The patients admitted in our center for anterior circulation acute ischemic stroke and (1) treated by mechanical thrombectomy with or without thrombolysis and (2) who underwent a baseline magnetic resonance imaging were included in the study. The SVD burden and the pial collaterality were assessed through the cerebral SVD score (severe when ≥1) and the Higashida score (favorable when ≥ 3) on magnetic resonance imaging and digital subtraction angiography, respectively. Any association between the cerebral SVD score and the collaterality were assessed through comparative and regression analyses. Results- Between January 2013 and March 2018, 240 patients met the inclusion criteria (68.7±16.1 years old; 49.2 % female). The cerebral SVD scores were of 0 in 125 (52.1%), 1 in 74 (30.8%), 2 in 30 (12.5%), and 3 in 11 (4.6%) patients. Hundred and thirty-six patients (58.1%) presented a favorable collaterality score. The favorable collaterality subgroup presented a significantly higher proportion of female (79%), lower baseline National Institutes of Health Stroke Scale ( P<0.001), and higher Diffusion-Weighted Imaging-Alberta Stroke Program Early CT Scores ( P<0.001). The regression analyses showed no impact of the cerebral SVD score on the collaterality pattern (odds ratio, 1.11, 95% CI, 0.82-1.50; P=0.51). Conclusions- In patients with anterior circulation acute ischemic stroke, collateral flow status does not seem to be influenced by SVD burden.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".