Perspectives of women on screening and prevention of CMV in pregnancy
Bibliographic record
Abstract
OBJECTIVE: To assess the choice and attitude of pregnant women regarding CMV serological screening and CMV prevention behaviors in pregnancy. STUDY DESIGN: In this cross-sectional study, pregnant women were recruited in a single center during routine prenatal screening tests at 11-16 weeks. Participants filled out a questionnaire assessing knowledge about congenital CMV (cCMV) infection, risk perception and willingness to have CMV serological screening as well as their attitude toward CMV prevention behaviors. RESULTS: Among 234 pregnant women, 74.4 % (95 % confidence interval: 68.8-80.0 %) wanted CMV serological screening in pregnancy. The factors significantly associated with the desire for screening were perceived risk and perceived severity of cCMV. An informed choice regarding CMV screening (value-consistent, based on good knowledge and deliberated) was performed by 54 % of women who chose the screening and 30 % of women who declined the screening (p = 0.039). The median scores regarding attitudes toward CMV prevention behaviors were 3.7/5 for avoiding sharing behaviors and 4.0/5 for not kissing a child on the lips. CONCLUSION: The majority of pregnant women want to have CMV serological screening once informed about congenital CMV infection. New tools need to be developed to allow for informed choice regarding CMV serological screening in pregnancy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".