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Record W3144814027 · doi:10.1159/000512761

Risk Factors for Failure in First-Time Hearing Screening Tests among High-Risk Neonates in Neonatal Intensive Care Unit

2021· article· en· W3144814027 on OpenAlexaff
Feng Zhai, Xuhua Fang, Yanbo Li, Haoliang Chen, Jie Chen

Bibliographic record

VenueAudiology and Neurotology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineNeonatal intensive care unitNeonatal respiratory distress syndromePediatricsRespiratory distressBirth weightGestational ageLow birth weightLogistic regressionStepwise regressionAuditory brainstem responseObstetricsPregnancyHearing lossInternal medicineSurgeryAudiology

Abstract

fetched live from OpenAlex

<b><i>Objective:</i></b> The aim of the study was to investigate into the risk factors for failure in the first-time screening test among high-risk neonates in neonatal intensive care unit (NICU) in order to further clarify the etiology of neonatal hearing impairment, thus providing insights into early prevention and intervention. <b><i>Methods:</i></b> We performed automated auditory brainstem response (AABR), distortion product otoacoustic emission (DPOAE), and acoustic immittance (AI) on 2,194 high-risk neonates admitted into the NICU of Shanghai Children’s Medical Center from January 2015 to December 2019, and the risk factors, including premature birth, hyperbilirubinemia, and infant respiratory distress syndrome, were analyzed retrospectively by the univariate χ<sup>2</sup> test and multivariate stepwise logistic regression analysis. <b><i>Results:</i></b> The pass rates of AABR, DPOAE, and AI were 70.21, 78.44, and 93.12%, respectively, in 2,194 cases of high-risk neonates screened, which are significantly lower than those of healthy controls. The most common diagnoses included artificial feeding, preterm birth, C-section, low birth weight (LBW), neonatal hyperbilirubinemia (NHB), neonatal respiratory distress syndrome (NRDS), congenital heart disease (CHD), gestational diabetes mellitus, pregnancy-induced hypertension syndrome, advanced maternal age (AMA), twins, and in vitro fertilization. Stepwise logistic regression analysis indicated that the AABR pass rate was negatively correlated with LBW (<i>p</i> = 0.002), NHB (<i>p</i> < 0.001), NRDS (<i>p</i> = 0.007), artificial or mixed feeding (<i>p</i> = 0.018), and CHD (<i>p</i> = 0.005). The pass rate of DPOAE was negatively correlated with artificial or mixed feeding (<i>p</i> = 0.041), NHB (<i>p</i> < 0.001), LBW (<i>p</i> = 0.007), very LBW (VLBW) (<i>p</i> = 0.008), and C-section (<i>p</i> < 0.001). The pass rate of AI was negatively correlated with revised AMA (≥40 year) (<i>p</i> < 0.001), NHB (<i>p</i> = 0.043), C-section (<i>p</i> = 0.005), and artificial/mixed feeding (<i>p</i> = 0.036). <b><i>Conclusion:</i></b> The hearing screening pass rates of high-risk neonates in the NICU were lower than those of normal neonates, among which the rate of AABR was significantly lower than that of DPOAE. NRDS, NHB, LBW, revised AMA, CHD, C-section, and artificial feeding are potential risk factors of hearing impairment. The combination of different hearing screening tests is necessary for accurate diagnosis of congenital hearing disorders.

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)0.000
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.031
GPT teacher head0.272
Teacher spread0.241 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2021
Admission routes1
Has abstractyes

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