The significant effects of cerebral microbleeds on cognitive dysfunction: An updated meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Accumulated data suggests that cerebral microbleeds (CMBs) play an important role in the decline of cognitive function, but the results remain inconsistent. In the current study, we aimed to investigate the association between CMBs and cognitive function, as well as the various effects of CMBs on different domains of cognition. METHODS: We searched through the databases of PubMed, Embase, Cochrane Library, and ScienceDirect. After a consistency test, the publication bias was evaluated and a sensitivity analysis was performed with combined odds ratios (OR) and standardized mean difference (SMD) of CMBs. RESULTS: A meta-analysis of 25 studies with 9343 participants total was conducted. Patients with CMBs had higher incidence of cognitive impairment (OR:3.5410; 95% confidence interval [CI] [2.2979, 5.4567], p<0.05) and lower scores of cognitive functions (SMD: -0.2700 [-0.4267, -0.1133], p<0.05 in Mini-Mental State Examination [MMSE] group and -0.4869 [-0.8902, -0.0818], p<0.05 in Montreal Cognitive Assessment [MoCA] group). Our results also indicated that patients with CMBs had obvious decline in cognitive functions, for instance, orientation (SMD: -0.9565 [-1.7260, -0.1869], p<0.05), attention and calculation (SMD: -1.1518 [-1.9553, -0.3484], p<0.05) and delayed recall (SMD: -0.5527 [-1.1043, -0.0011], p = 0.05). CONCLUSIONS: Our data suggested that CMBs might be an important risk factor for cognitive dysfunction, especially in the domains of orientation, attention and calculation and delayed recall functions. Prospective cohort studies with further investigations will be needed in larger samples.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.046 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.004 | 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".