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Record W2044579073 · doi:10.1258/0022215021910014

Congenital conductive hearing loss

2002· article· en· W2044579073 on OpenAlexaff
Eyal Raveh, Weili Hu, Blake C. Papsin, Vito Forte

Bibliographic record

VenueThe Journal of Laryngology & Otology · 2002
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsConductive hearing lossAudiologyHearing lossElectrical conductorMedicineMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Congenital conductive hearing loss due to ossicular deformities can be treated by either rehabilitation with a hearing aid or surgical reconstruction. We present the results of exploratory tympanotomy performed in a large paediatric otolaryngology centre in 67 patients with non-serous congenital conductive hearing loss. Forty-two children had malformation of one or more ossicles without fixation of the stapes, and 19 had fixed stapes. In 26 cases, the surgeon decided not to perform surgical correction. Seven operated patients were lost to follow-up. As a group, 47 per cent of the patients who underwent reconstruction showed no significant benefit from surgery, with post-operative air-bone gaps (ABG) greater than 30 dB. Assessment of the results by pathology showed that 64 per cent of the patients with mobile stapes had an air-bone gap within 30 dB compared to only 33 per cent of the patients with fixed stapes. One patient sustained severe sensorineural loss after the procedure. Considering that exploratory tympanotomy is a relatively minimal, benign procedure but that findings during exploration may exclude the option of reconstruction (in 39 per cent of our patients), we suggest exploring the ear, but in a more realistic, informed way.

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)0.003
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.076
GPT teacher head0.290
Teacher spread0.214 · 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

Citations63
Published2002
Admission routes1
Has abstractyes

Explore more

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