Imaging changes in cerebral small vessel disease and risk factors of Alzheimer's disease: Meta⁃analysis
Bibliographic record
Abstract
Objective To evaluate the imaging changes in patients with cerebral small vessel disease(cSVD) and the risk factors of Alzheimer's disease(AD). Methods Retrieve relevant retrospective case analysis and observational studies that about cSVD and the risk factors of AD from online database (January 1, 1980-April 1, 2019) as PubMed, EMBASE/SCOPUS and Cochrane Library with key words: Alzheimer's disease, cerebral small vessel disease, white matter lesion, cerebral microbleeds, lacunar infarction. Selection of studies was performed according to pre⁃designed inclusion and exclusion criteria. Quality of studies was evaluated by using Newcastle⁃Ottawa Scale (NOS). All data were pooled by Stata 15.1 software for Meta⁃analysis. Results A total of 1452 articles were enrolled, from which 11 studies with NOS score≥5 were chosen after excluding duplicates and those not meeting the inclusion criteria. A total of 12882 patients were included. Meta⁃analysis showed that white matter lesion (RR=1.152,95%CI: 1.007-1.317; P=0.039) and cerebral microbleeds (RR=1.659, 95%CI: 1.085-2.537; P=0.019)increased the risk of AD;lacunar infarc did not increase the risk of AD(RR=1.298,95%CI:0.785-2.147; P=0.309). Conclusions The risk of Alzheimer's disease is increased in patients with white matter lesion and cerebral microbleeds. The cerebral small vessel disease may be involved in the occurrence of some Alzheimer's disease. When patients have the imaging changes of white matter lesion and cerebral microbleeds, should be paid on primary prevention of Alzheimer's disease.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.059 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".