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Record W2325306680 · doi:10.1055/s-0031-1291936

Trends in Congenital Cytomegalovirus: A Review of Current Screening Methods and Prevention Strategies

2011· review· en· W2325306680 on OpenAlexaboutno aff
Francine Tvrdy, Troy Johnson, Jeff Hoffman, Julie A. Honaker, Stephen Boney

Bibliographic record

VenueSeminars in Hearing · 2011
Typereview
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSensorineural hearing lossNewborn screeningCytomegalovirusHearing lossPediatricsIncidence (geometry)Intensive care medicineAudiologyImmunologyViral diseaseHerpesviridaeVirus

Abstract

fetched live from OpenAlex

Cytomegalovirus (CMV) is one of the leading causes of congenital sensorineural hearing loss. Currently, ∼40,000 infants in the United States are infected annually with CMV, and of these 40,000 infants, upward of 6000 infants will develop sensorineural hearing loss. Most of these infants will go undetected for congenital hearing loss by a newborn screening program, due to having late-onset or progressive hearing loss. An efficient CMV screening program of newborns will help to identify those infected and at risk for developing sensorineural hearing loss. Also, it will allow close monitoring of these infants for maximum speech and language development. Reliable methods are needed for an effective CMV screening program. Because the dried blood spot (DBS) sample is routinely collected at birth for metabolic screenings, there is growing interest to adapt this as the universal screening method. However, sensitivity of the DBS in detecting CMV has varied, and recent evidence has shown it less reliable than urine or saliva analysis. Further research is needed to develop the most efficient, reliable, and cost-effective programs. Models such as the Quebec metabolic screening program may ensure earlier identification of CMV. We are closer to a means of prophylactic prevention with a CMV vaccine; however, increased patient education of CMV prevention by health care professionals, including audiologists, is the current best practice for reducing the incidence of CMV infection.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.209
GPT teacher head0.503
Teacher spread0.293 · 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 teacher head, not a consensus.

Study designOther design
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

Citations0
Published2011
Admission routes1
Has abstractyes

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