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Record W2051339839 · doi:10.2310/7070.2005.4127

Transient Evoked Otoacoustic Emissions in Newborn Infants: Effects of Ear Asymmetry, Gender, and Age

2006· article· en· W2051339839 on OpenAlexvenueno aff
Yuko Saitoh, Takema Sakoda, Michio Hazama, Hiroko Funakoshi, Hiroki Ikeda, Akira Shibano, Shinji Yajin, Shigetoshi Yoda, Yoshihiro Dake, Tadao Enomoto, Hiroya Kitano

Bibliographic record

VenueThe Journal of Otolaryngology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsnot available
FundersOklahoma Agricultural Experiment Station
KeywordsReproducibilityMedicineAudiologyNoise (video)Statistics

Abstract

fetched live from OpenAlex

Our aim was to examine the effects of gender, ear asymmetry, and age of infants on various parameters of transient evoked otoacoustic emissions (TEOAEs). Three hundred thirty-two infants (181 males, 151 females) were tested using the ILO292 Otodynamics Analyzer (Otodynamics Ltd, England) as a screening procedure. The subjects were divided into two age groups: group 1, newborn infants prior to hospital discharge (mean age of 4 days), and group 2, infants at the 1-month-old health checkup (mean age of 35 days). Responses to TEOAE stimuli were recorded at 1.0, 1.5, 2.0, 3.0, and 4.0 kHz. There were significant effects of gender and ear (left/right) on the signal-to-noise ratio, response level, and whole-wave and band reproducibility values in TEOAEs. The right ear had higher values of whole-wave reproducibility, response level, signal-to-noise ratio, and band reproducibility than the left ear. Females displayed higher whole-wave reproducibility, response level, band reproducibility, and signal-to-noise ratio values than males. There was no significant difference in response level, signal-to-noise ratio, and band reproducibility between the two age groups. The findings of the present investigation may contribute toward future improvements in neonatal hearing screening based on the community.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.260
Teacher spread0.243 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations34
Published2006
Admission routes1
Has abstractyes

Explore more

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