Seroconversion for cytomegalovirus infection in a cohort of pregnant women in Québec, 2010–2013
Bibliographic record
Abstract
Cytomegalovirus (CMV) is the leading cause of congenital infection and non-genetic sensorineural hearing loss in children. There are no recent data on the incidence of CMV infection during pregnancy in Canada. This present study was undertaken to determine the seroprevalence of CMV IgG antibodies and the rate of seroconversion in a cohort of pregnant women in the province of Québec, Canada. We used serum samples and questionnaire data collected as part of the 3D Pregnancy and Birth Cohort Study (2010-2013) conducted in Québec, Canada. CMV IgG antibodies were determined in serum samples collected at the first and third trimesters. Associations between independent variables and seroprevalence were assessed using logistic regression, and associations with seroconversions, by Poisson regression. Of 1938 pregnant women tested, 40·4% were seropositive for CMV at baseline. Previous CMV infection was associated with: working as a daycare educator, lower education, lower income, having had children, first language other than French or English, and being born outside Canada or the United States. Of the 1122 initially seronegative women, 24 (2·1%) seroconverted between their first and third trimesters. The seroconversion rate was 1·4 [95% confidence interval (CI) 0·9-2·1]/10 000 person-days at risk or 3·9 (95% CI 2·5-5·9)/100 pregnancies (assuming a 280-day gestation). The high proportion of pregnant women susceptible to CMV infection (nearly 60%) and the subsequent rate of seroconversion are of concern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".