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Record W2080059037 · doi:10.3109/14992027.2012.660575

<i>GJB2</i>(Connexin-26) mutations are not frequent among hearing impaired patients in East Greenland

2012· article· en· W2080059037 on OpenAlexaboutno aff
Preben Homøe, Anders Koch, Nanna Dahl Rendtorff, Marianne Lodahl, Ture Andersen, Stig Andersen, Hans Eiberg, Inge-Merete Nielsen, Lisbeth Tranebjærg

Bibliographic record

VenueInternational Journal of Audiology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyMedicineConnexinHearing impairedHearing lossBiologyGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE: Investigate genetic causes of HI among the Inuit populations in the Arctic with a high prevalence of hearing impairment (HI). DESIGN: A cross-sectional survey with population-based controls. STUDY SAMPLE: Forty-five patients, with sensorineural or mixed HI and an available blood sample for GJB2 sequencing from DNA, were selected from 166 east Greenlanders by specialist audiology examination, including pure-tone air and bone conduction audiometry from 125 Hz to 8000 Hz. Controls were 108 east- and 109 west-Greenlanders. RESULTS: Forty-five patients with HI were included, 24 males and 21 females. Median age was 35 years (range: 5-76). The c.35delG allele frequency was 3.3%. One patient, homozygous for the c.35delG GJB2 mutation, had bilateral congenital profound HI. Another with mixed HI was heterozygous for the same mutation. Three were heterozygous for the p.V27I variant and one was heterozygous for the p.V153I variant. The frequency of the c.35delG mutation in the controls varied between 0.5% in west Greenland to 2.3% in east Greenland. CONCLUSION: The c.35delG GJB2 mutation occurs in Greenland with low frequency. We conclude the main causes behind the prevalence of HI in this population are chronic otitis media, noise traumas, and/or unidentified genetic causes.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0000.000
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 head0.057
GPT teacher head0.304
Teacher spread0.247 · 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 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

Citations10
Published2012
Admission routes1
Has abstractyes

Explore more

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