The urgent search for predictive biomarkers in the emerging era of universal congenital cytomegalovirus screening
Bibliographic record
Abstract
acquisition of cytomegalovirus (CMV) represents the most common infectious cause of paediatric developmental disability. With a global prevalence of approximately 0.7%, congenital CMV (cCMV) infection can produce wide-ranging injury to the developing fetal and neonatal central nervous system, leading to microcephaly, intracranial calcifications, neuronal migration defects and damage to the developing cochlea and retina. Clinical sequelae include cerebral palsy, seizure disorder, intellectual disabilities, developmental delay, autism spectrum disorders, sensorineural hearing loss (SNHL) and visual impairment. It has been generally believed that most cCMV infections are asymptomatic in nature, and are not associated with long-term neurodevelopmental impairment. This dogma, however, has been called into question in the context of several state and provincial universal cCMV screening programmes that have been implemented in recent years in the United States and Canada. Moreover, the full spectrum of neurodevelopmental sequelae amongst asymptomatic cCMV cases is just starting to be recognized. Host and/or viral factors that predict which asymptomatic infants will have sequelae, including SNHL, are unknown. This review summarizes the current state of the art with respect to the search for predictive biomarkers that can inform the prognosis of asymptomatic cCMV, and aid in decision-making about therapeutic intervention.This article is part of the discussion meeting issue 'The indirect effects of cytomegalovirus infection: mechanisms and consequences'.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".