Effect of leukoaraiosis on collateral circulation in acute ischemic stroke treated with endovascular therapy: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: The recruitment of collateral circulation correlates with a balance of the microvasculature. Uncertainty remains to be made about the association of leukoaraiosis with leptomeningeal collaterals. To explore the effect of leukoaraiosis on leptomeningeal collaterals in patients treated with endovascular therapy. METHODS: Observational studies exploring the correlation between leukoaraiosis and leptomeningeal collaterals in large vessel occlusion treated with endovascular therapy were searched from PubMed, EMBASE, and Cochrane Libraries databases. Two independent reviewers retrieved eligible literature, extracted purpose-related data, and utilized the Newcastle-Ottawa Scale to evaluate the risk of bias. A Mantel-Haenszel method was used to calculate the odds ratio (OR). Meta-regression and subgroup analyses were conducted to clarify heterogeneity. RESULTS: Data from 10 studies with 1606 patients were extracted for pooled analysis. Compared to non-severe leukoaraiosis, patients with severe leukoaraiosis showed significant relevance to poor leptomeningeal collaterals (OR, 2.13; 95% confidence interval [1.27-3.57]; P = 0.004). Meta-regression indicated that sample size (coefficient = -0.007299, P = 0.035) and the number of female patients (coefficient = -0.0174709, P = 0.020) were sources of heterogeneity. Furthermore, all of the countries (USA versus France versus China, Q = 3.67, P = 0.159), various assessment scales of leukoaraiosis (the Fazekas scale versus Non-Fazekas scales, Q = 0.77, P = 0.379), and different imaging methods of leukoaraiosis (computed tomography versus magnetic resonance imaging, Q = 2.12, P = 0.146) and leptomeningeal collaterals (computed tomography angiography versus digital subtraction angiography, Q = 1.21, P = 0.271) showed no contribution to the effect size. CONCLUSION: Severe leukoaraiosis is associated with poor leptomeningeal collaterals in patients treated with endovascular therapy. Further studies may focus on whether the finding applies to different stroke subtypes.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".