Clinical Factors Associated With Congenital Cytomegalovirus Infection: A Cohort Study of Pregnant Women and Newborns
Bibliographic record
Abstract
BACKGROUND: The aim of this prospective cohort study was to determine clinical factors associated with the occurrence of congenital cytomegalovirus infection (cCMV) in pregnant women. METHODS: Between March 2009 and November 2017, newborns born at a primary maternity hospital received polymerase chain reaction (PCR) analyses for CMV DNA in their urine with informed consent of the mothers at a low risk. Clinical data, including age, gravidity, parity, body mass index, occupation, maternal fever/flulike symptoms, pregnancy complications, gestational weeks at delivery, birth weight, and automated auditory brainstem response, were collected. Logistic regression analyses were performed to determine clinical factors associated with cCMV. RESULTS: cCMV was diagnosed by positive PCR results of neonatal urine in 9 of 4125 pregnancies. Univariate and multivariable analyses revealed that the presence of fever/flulike symptoms (odds ratio [OR], 17.9; 95% confidence interval [CI], 3.7-86.7; P < .001) and threatened miscarriage/premature labor in the second trimester (OR, 6.0; 95% CI, 1.6-22.8; P < .01) were independent clinical factors associated with cCMV. Maternal fever/flulike symptoms or threatened miscarriage/premature labor in the second trimester had 100% sensitivity, 53.2% specificity, and a maximum Youden index of .85. CONCLUSIONS: This cohort study for the first time demonstrated that these clinical factors of pregnant women and newborns were associated with the occurrence of cCMV. This is useful information for targeted screening to assess risks of cCMV in low-risk mothers, irrespective of primary or nonprimary CMV infection.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".