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Record W4387523897 · doi:10.1097/moo.0000000000000925

Newborn cytomegalovirus screening: is this the new standard?

2023· review· en· W4387523897 on OpenAlexaff
Soren Gantt

Bibliographic record

VenueCurrent Opinion in Otolaryngology & Head & Neck Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineCytomegalovirusNewborn screeningAsymptomaticPediatricsScreening testPsychological interventionIdentification (biology)Intensive care medicineVirologySurgeryVirusHerpesviridaeViral diseaseNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Congenital cytomegalovirus infection (cCMV) is a major cause of childhood hearing loss and neurodevelopmental delay. Early identification of cCMV allows for interventions that improve outcomes, particularly for cCMV-related hearing loss that develops in early childhood. Most cCMV is asymptomatic at birth and is rarely diagnosed without newborn screening. Therefore, various approaches to cCMV screening are increasingly being adopted. RECENT FINDINGS: Both universal screening (testing all newborns) and targeted screening (testing triggered by failed hearing screening) for cCMV appear valuable, feasible and cost-effective, though universal screening is predicted to have greatest potential overall benefits. CMV PCR testing of newborn oral swabs is sensitive and practical and is therefore widely used in targeted screening programs. In contrast, PCR using dried-blood spots (DBS) is less sensitive but was adopted by current universal cCMV screening initiatives because DBS are already collected from all newborns in high-income countries, which circumvents large-scale oral swab collection. SUMMARY: Targeted screening is widely recommended as standard of care, while universal screening is less common but is progressively considered as the optimal strategy for identification of children with cCMV. As with all screening programs, cCMV screening requires commitments to equitable and reliable testing, follow-up and services.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.200
GPT teacher head0.438
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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