Cytomegalovirus Infection and Alzheimer's Disease: A Meta-Analysis
Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it