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Record W1969164058 · doi:10.1002/ajmg.a.20588

Large deletion of the <i>GJB6</i> gene in deaf patients heterozygous for the <i>GJB2</i> gene mutation: Genotypic and phenotypic analysis

2004· article· en· W1969164058 on OpenAlexaff
Delphine Feldmann, Françoise Denoyelle, Pierre Chauvin, Eréa‐Noël Garabédian, Rémy Couderc, Sylvie Odent, Alain Joannard, S. Schmerber, Bruno Delobel, Jacques Leman, Hubert Journel, Hélène Catros, Cédric Le Maréchal, Hélène Dollfus, Marie‐Madeleine Eliot, Jean‐Pierre Delaunoy, Albert David, C. Calais, Valérie Drouin‐Garraud, Marie-Françoise Obstoy, D. Bouccara, Olivier Sterkers, Patrice Tran Ba Huy, Cyril Goizet, F Duriez, Florence Fellmann, J Hélias, Jacqueline Vigneron, Bétina Montaut, Patricia Lewin, Christine Petit, Sandrine Marlin

Bibliographic record

VenueAmerican Journal of Medical Genetics Part A · 2004
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsHotel Dieu Hospital
FundersFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche Médicale
KeywordsGeneticsMutationPhenotypeHearing lossGeneGenotypeHeterozygote advantageBiologyGenotype-phenotype distinctionCompound heterozygosityAudiologyMedicine

Abstract

fetched live from OpenAlex

Recent investigations identified a large deletion of the GJB6 gene in trans to a mutation of GJB2 in deaf patients. We looked for GJB2 mutations and GJB6 deletions in 255 French patients presenting with a phenotype compatible with DFNB1. 32% of the patients had biallelic GJB2 mutations and 6% were a heterozygous for a GJB2 mutation and a GJB6 deletion. Biallelic GJB2 mutations and combined GJB2/GJB6 anomalies were more frequent in profoundly deaf children. Based on these results, we are now assessing GJB6 deletion status in cases of prelingual hearing loss.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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 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

Citations49
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Medical Genetics Part ASame topicHearing, Cochlea, Tinnitus, GeneticsFrench-language works237,207