White Matter Hyperintensities and Functional Outcomes in Patients With Cerebral Hemorrhage: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: There are controversies about white matter hyperintensities (WMH) and the prognosis of spontaneous intracerebral hemorrhage. Our objective is to investigate the relationship between WMH and functional outcomes after intracerebral hemorrhage (ICH). Methods: We systematically searched PubMed, EMBASE, and Cochrane Library databases from inception through August 10, 2021 without any restriction of countries. Articles investigating the relationship of WMH and functional outcomes as well as mortality of patients with spontaneous ICH were included. We extracted relevant data and evaluated the study quality with the Newcastle-Ottawa Scale. We pooled odds ratio (OR) for the presence and different severities of WMH with random effects models using STATA. Results: A total of 10,584 patients with ICH in 18 studies were included in the analysis. Moderate/severe WMH were related to poor functional outcome [OR, 1.805, 95% confidence interval (CI), 1.320-2.469] and all-cause mortality (OR, 3.27, 95% CI, 2.07-5.18) after ICH. Besides, the increasing severity of WMH was also related to poor functional outcome (OR, 1.34, 95% CI, 1.17-1.53) and all-cause mortality (OR, 1.62, 95% CI, 1.39-1.90). The pooled data did not find the relationship between the presence of WMH and poor functional outcome (OR, 2.54, 95% CI, 0.91-7.05) after ICH. The results remained stable after adjusting for age, hematoma volume, stroke, and intraventricular hemorrhage. Conclusion: We found moderate and severe WMH were related to poor functional outcomes and all-cause mortality after ICH. High-quality prospective studies are still needed. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021278409.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".