OP01.04: Ultrasound brain abnormalities in fetuses with congenital cytomegalovirus infection: systematic review and meta‐analysis
Bibliographic record
Abstract
The incidence of fetal abnormalities secondary to congenital cytomegalovirus (CMV) infection is likely to be overestimated due to bias in reporting cases where fetal abnormalities detected on ultrasound (US) triggered testing for CMV. The main aim of this study was to quantify fetal brain US abnormalities secondary to congenital CMV infection. Medline, Embase and the Cochrane library were searched electronically utilising combinations of the relevant medical subject heading for “Cytomegalovirus infection” and “ultrasound”. Studies reporting data on the prenatal brain US findings in fetuses with confirmed congenital CMV infection were included. The outcomes assessed included all brain abnormalities associated with congenital CMV infection. Quality assessment of the included studies was performed using the Newcastle-Ottawa Scale (NOS) for cohort studies. Meta-analyses of proportions were used to combine data. Between-study heterogeneity was explored using the I statistic. A total of 982 articles were identified, 48 were assessed with respect to their eligibility for inclusion and total of 7 studies were included in the study. The most common brain US abnormalities detected during the 2nd trimester of pregnancy were: white matter abnormalities (17.6%), ventriculomegaly (9.3%), microcephaly (2.7%) and brain cysts (2.7%). The most common brain US abnormalities detected during the 3rd trimester of pregnancy were: brain calcification (31.5%), ventriculomegaly (26.6%) and white matter abnormalities (29.3%). Less common were microcephaly (8.1%), brain cyst (4.9%), intraventricular synechiae (8.7%) and cortical migration abnormalities (8.7%). The most frequent US brain lesions associated with congenital CMV, both in 2nd and 3rd trimester of pregnancy, are white matter abnormalities. Large prospective studies are needed in order to ascertain the correlation between each of these abnormalities and the long term neurodevelopmental outcome.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".