White matter hyperintensities and their subtypes in patients with carotid artery stenosis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We aimed to perform a systematic review and meta-analysis to clarify the association between white matter hyperintensities (WMHs) and carotid artery (CA) stenosis. STUDY DESIGN: Systematic review and meta-analysis. PARTICIPANTS: CA stenosis was set at ≥50%, and WMHs were assessed by MRI and evaluated quantitatively or semiquantitatively. DATA SOURCES: A comprehensive literature search was performed in PubMed, EMBASE and Cochrane Library for studies evaluating the association between WMHs and CA stenosis ≥50% from inception to 13 September 2017. MAIN OUTCOMES AND MEASURES: Standardised mean difference (SMD) with 95% CI was used to evaluate the association between WMHs and CA stenosis. Results were presented in a forest plot with a fixed-effects model or random-effects model. We assessed the quality of included studies using the Newcastle-Ottawa Scale. Funnel plots and Egger's and Begg's tests were conducted to assess publication bias. Sensitivity analysis was performed to evaluate the influence of each individual study. RESULTS: Eight studies enrolling 677 patients were included. There was a positive relationship between the total WMHs and CA stenosis, with a pooled fixed-effects SMD of 0.326 (95% CI 0.194 to 0.459, p=0.000). Heterogeneity and publication bias were low among these studies. Subgroup analysis of three studies enrolling 225 patients showed an association between periventricular WMHs and CA stenosis, with a pooled fixed-effects SMD of 0.412 (95% CI 0.202 to 0.622, p=0.000). CONCLUSION: This meta-analysis showed that the total WMHs and periventricular WMHs were associated with CA stenosis. WMHs may be considered as an individual risk stratification score when choosing a proper plan for therapy of CA stenosis.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".