Bibliographic record
Abstract
Online Supplementary data for the following article:\n\nGeorgakis MK, Duering M, Wardlaw JM, Dichgans M. WMH and long-term outcomes in ischemic stroke: a systematic review and meta-analysis.\n\nCONTENTS:\ne-Methods.\nTable e-1. Excluded articles and reasons for exclusion.\nTable e-2. Articles excluded due to overlapping populations with other eligible studies.\nTable e-3. Characteristics of eligible articles by examined outcomes.\nTable e-4. Quality score of eligible articles by examined outcomes, as assessed by the Newcastle Ottawa scale.\nTable e-5. Sensitivity analyses restricted to studies fulfilling each of the Newcastle-Ottawa scale quality prerequisites per scale item. \nTable e-6. Meta-regression analyses for the effect of different study characteristics on the association of white matter hyperintensities at baseline (moderate/severe vs. mild/none) with stroke outcomes. \nTable e-7. Assessment of publication bias with the Egger’s test in the analysis of the associations of white matter hyperintensities (WMH) with stroke outcomes. \nFigure e-1. Forest plots depicting the associations of white matter hyperintensities (WMH) with (A) dementia and (B) cognitive impairment.\nFigure e-2. Forest plots depicting the associations of white matter hyperintensities (WMH) with (A) any functional impairment, (B) functional impairment defined as mRS >1, and (C) functional impairment defined as mRS >2.\nFigure e-3. Forest plots depicting the associations of white matter hyperintensities (WMH) with (A) any recurrent stroke, and (B) recurrent ischemic stroke.\nFigure e-4. Forest plots depicting the associations of white matter hyperintensities (WMH) with (A) all-cause mortality, and (B) cardiovascular mortality.\nFigure e-5. Forest plots depicting the associations of white matter hyperintensities (WMH) volume with any functional impairment.\nFigure e-6. Forest plots depicting the summary association estimates between white matter hyperintensities (WMH) and study outcomes, when excluding studies based on secondary analyses of randomized controlled trials.\nFigure e-7. Forest plots depicting the summary association estimates between white matter hyperintensities (WMH) and study outcomes derived from sensitivity analyses restricted to studies that have analyzed their data with Cox regression models and have provided Hazard Ratios as estimates of association.\nFigure e-8. Dose-response meta-analysis of the association of white matter hyperintensities burden at baseline with stroke outcomes in studies adjusting for age, stroke severity and cardiovascular risk factors. The graphs depict the restricted cubic spline derived effect estimates and their 95% confidence intervals for (A) dementia, (B) any functional impairment, (C) any recurrent stroke, and (D) all-cause mortality.\nFigure e-9. Funnel plots for the associations of white matter hyperintensities (WMH) with (A) dementia, (B) any functional impairment, (D) any recurrent stroke, and (D) all-cause mortality.\ne-References.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.917 | 0.649 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".