A Meta-Analysis of Association between Cerebral Microbleeds and Cognitive Impairment
Bibliographic record
Abstract
Background: The clinical effect of cerebral microbleeds (CMBs) on cognition has been receiving much research attention, but results are often inconsistent. Material/Methods: We searched PubMed, Embase, Web of Science, and some Chinese electronic databases. A total of 15 studies were included. Results: Patients with CMBs had higher incidence of cognitive dysfunction (OR 3.14; 95 % CI 1.66–5.92) and lower scores of cognitive function (SMD was –0.36 [–0.55, –0.18] in the MMSE group and –0.65 [–0.99, –0.32] in the MoCA [Montreal Cognitive Assessment] group). The results also indicated that a higher number of CMB lesions led to more severe cognitive dysfunction (SMD was –2.41 [–5.04, –0.21] in the mild group and –2.75 [–3.50, –2.01] in the severe group). We also found that cognitive performance was significantly impaired when CMBs were lo-cated in deep (–0.4 [–0.69, –0.11]), lobar regions (–0.50 [–0.92, –0.09]), basal ganglia (–0.72 [–1.03, –0.41]), and thalamus brain regions (–0.65 [–0.98, –0.32]). Conclusions: This meta-analysis showed that CMBs were associated with cognitive dysfunction according to higher number and different locations of CMBs. Future work should focus on long-term prognosis of continuing cognitive de-
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".