Seroepidemiology of Cytomegalovirus Infection in Pregnant Women in the Central Mexican City of Aguascalientes
Bibliographic record
Abstract
BACKGROUND: Infection with cytomegalovirus (CMV) during pregnancy may lead to congenital disease. Very little is known about the seroepidemiology of CMV infection in pregnant women in Mexico. We sought to determine the seroprevalence and correlates of CMV infection in pregnant women in Aguascalientes City, Mexico. METHODS: Through a cross-sectional study design, 289 pregnant women were examined for anti-CMV IgG and IgM antibodies in Aguascalientes City, Mexico. A standardized questionnaire was used to obtain the socio-demographic, clinical and behavioral characteristics of the pregnant women. The association between CMV infection and the characteristics of the pregnant women was assessed by bivariate and multivariate analyses. RESULTS: Anti-CMV IgG antibodies were detected in 259 (89.6%) of the 289 pregnant women studied. None of the 289 pregnant women were positive for anti-CMV IgM antibodies. Seroprevalence of CMV infection was significantly lower (P = 0.03) in pregnant women with reflex impairment (5/8: 62.5%) than in those without this clinical feature (246/272: 90.4%). Seroprevalence of CMV infection was significantly higher (P = 0.03) in pregnant women with 2 - 9 pregnancies (140/150: 93.3%) than in those with only one pregnancy (119/139: 86.2%). Logistic regression analysis of socio-demographic and behavioral variables showed that seropositivity to CMV was associated with contact with children (odds ratio (OR) = 3.56; 95% confidence interval (CI): 1.17 - 10.84; P = 0.02), whereas high (> 150 AU/mL) anti-CMV antibody levels were negatively associated with washing hands before eating (OR = 0.17; 95% CI: 0.05 - 0.63; P = 0.007). CONCLUSIONS: We found a high endemicity of CMV infection in pregnant women in Aguascalientes City, Mexico. Factors associated with CMV infection found in this study may be useful for an optimal planning of preventive measures against CMV exposure in pregnant women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".