An association between cytomegalovirus infection and pre‐eclampsia: a case–control study and data synthesis
Bibliographic record
Abstract
OBJECTIVE: Pre-eclampsia shares several similarities with atherosclerotic heart disease. We explored whether, like atherosclerosis, there is a potential link between cytomegalovirus (CMV) infection and pre-eclampsia. DESIGN: CMV IgG, IgM and IgA antibodies were determined by enzyme-linked immunosorbent assays in serums from pre-eclampsia (n = 78), normotensive intrauterine growth restriction (nIUGR) (n = 30) and normal pregnancy controls (n = 109). Data were analyzed by chi-squared, Kruskal-Wallis ANOVA and Mann-Whitney U-tests. Further, we conducted a comprehensive review of published studies on the relation between CMV infection and pre-eclampsia. Risk ratios (RRs) and 95% confidence interval (CI), according to CMV infection status, were calculated using Review Manager. MAIN OUTCOME MEASURES: Women with pre-eclampsia had increased CMV IgG seropositivity compared with nIUGR (p < 0.01) and normal pregnancy controls (p < 0.01). In addition, CMV IgG antibody level was higher in pre-eclampsia than normal pregnancy controls (p < 0.001). No difference was observed in CMV IgM or IgA among study groups. Data synthesis revealed that women with CMV infection were at higher risk in the development of pre-eclampsia, compared with women without CMV infection. Combined results for six studies yielded a RR of 1.5 (95% CI 1.2-1.9). CONCLUSION: CMV infection seems to affect the occurrence of pre-eclampsia. Evaluation of the relation between CMV infection and pre-eclampsia may provide mechanistic insights into pre-eclampsia-related inflammation.
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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 | 0.022 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".