Prevalence of Brain Microbleeds in Alzheimer Disease: A Systematic Review and Meta-Analysis on the Influence of Neuroimaging Techniques
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The literature on the prevalence of Alzheimer disease-associated cerebral microbleeds assessed with MR imaging shows considerable heterogeneity in terms of imaging techniques and parameters. Our aim was to perform a meta-analysis of the role of imaging techniques, including image acquisition, field strength and scanner type, and clinical and demographic factors on the reported prevalence of microbleeds in Alzheimer disease. MATERIALS AND METHODS: The prevalence of microbleeds was examined with respect to a priori-selected moderating variables via meta-analytic tools of literature reports. RESULTS: Fourteen unique studies providing 15 microbleed prevalence rates met the selection criteria for inclusion. The aggregate prevalence of microbleeds was 24% (95% CI, 19%-28%). Scan (SWI = 40%, gradient echo = 18%, EPI = 19%) and field strength (slope = 0.39; standard error = 15, P < .01) influenced the prevalence of microbleeds. The associations between microbleeds and age, sex, and global cognitive status were not significant. After updating the literature, the aggregate prevalence remained in the 95% CI range. CONCLUSIONS: Imaging technique and field strength are strongly associated with the prevalence of microbleeds over the global aggregate. Standardized imaging protocols for identification of microbleeds are recommended to minimize confounds.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".