Current status of primary, secondary and tertiary prevention of congenital cytomegalovirus disease: a call to action
Bibliographic record
Abstract
PURPOSE OF REVIEW: Globally, sequelae of congenital CMV (CCMV) impact an estimated 350 000 children born annually. In this review, we consider new evidence across primary, secondary and tertiary prevention approaches, and remaining evidence gaps. RECENT FINDINGS: Education on hygiene precautions can reduce risk of primary CMV acquisition in pregnancy, and may have a role in some settings in reducing CCMV cases resulting from nonprimary infection, but public and health worker knowledge and awareness remains low. Evidence that valaciclovir treatment can reduce CMV vertical transmission has led to renewed interest in antenatal CMV screening in some high-income countries over recent years, although there is a lack of recommendation in most guidelines and significant evidence gaps remain. Newborn CCMV screening has been adopted in some states/provinces in Canada/USA, with first results recently published. Newborn prognostic scoring systems are evolving, with potential for more effective targeting of newborn treatment and tertiary prevention of CCMV disease. SUMMARY: We make suggestions for clinical practice and research, particularly to address evidence gaps around: safety and effectiveness of antenatal CMV screening and antiviral prophylaxis; findings relating to detection of nonprimary infection in pregnancy; new prognostic neonatal scoring systems; and learning from follow-up of children born into state-wide universal CMV screening programmes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".