Cytomegalovirus Infection and Alzheimer's Disease: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Evidence on the association of cytomegalovirus (CMV) infection with Alzheimer's disease (AD) is scarce and the results are inconsistent. OBJECTIVE: To investigate the association of CMV infection with the risk of AD. METHODS: Observational studies on the relationship between CMV infection and AD were identified from PubMed, Embase, Web of Science, and the Cochrane Library until September 30, 2022. The quality of included studies was assessed using the Newcastle-Ottawa Scale. Random-effect meta-analysis was performed using a generic inverse-variance method, followed by sensitivity analyses and subgroup analyses based on study designs, regions, adjustments, and population types. RESULTS: Our search yielded 870 articles, of which 200 were duplicates and 663 did not meet the inclusion criteria, and finally yielded seven studies with 6,772 participants. No strong evidence was observed in the summary analysis for the association of CMV infection and risk of AD (odds ratio [OR] = 1.33; 95% confidence interval [CI]: 0.88, 2.03, I2 =69.9%). However, subgroup analysis showed that an increased risk of AD was detected in East Asians (OR = 2.39; 95% CI: 1.63, 3.50, I2 = 0.00%), cohort studies (OR = 1.99; 95% CI: 1.35, 2.94, I2 = 28.20%), and studies with confounder adjustment (OR = 2.05; 95% CI: 1.52, 2.77, I2 = 0.00%). CONCLUSIONS: This meta-analysis provides evidence to support the heterogeneity of the associations between CMV infection and AD. Future studies with larger sample sizes and multi-ethnic populations are necessary.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".