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Record W4210565530 · doi:10.1016/j.ijom.2022.01.005

Hearing impairment and ear anomalies in craniofacial microsomia: a systematic review

2022· review· en· W4210565530 on OpenAlexaff
W. Rooijers, P A E Tio, Marc P. van der Schroeff, Bonnie L. Padwa, David Dunaway, C.R. Forrest, Maarten J. Koudstaal, Cornelia J.J.M. Caron

Bibliographic record

VenueInternational Journal of Oral and Maxillofacial Surgery · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineMicrotiaCraniofacialHearing lossConductive hearing lossHemifacial microsomiaAudiologySensorineural hearing lossMiddle earCraniofacial abnormalityAtresiaAnatomy

Abstract

fetched live from OpenAlex

The aim of this systematic review was to review the literature on hearing impairment and ear anomalies in patients with craniofacial microsomia and to determine their prevalence. Sixty-two records including 5122 patients were included. Ear anomalies were present in 52-100% of patients. The most reported external ear malformations were microtia, pre-auricular tags, and atresia of the external auditory canal. Ossicular anomalies were the most reported middle ear malformations, whereas the most reported inner ear malformations included oval window anomalies, cochlear anomalies, and anomalies of the semicircular canals. Hearing loss in general was reported in 29-100% of patients, which comprised conductive hearing loss, mixed hearing loss, and sensorineural hearing loss. Between 21% and 51% of patients used hearing aids, and 58% underwent a surgical intervention to improve hearing. The relationship between different phenotypes of craniofacial microsomia and the type and severity of hearing loss is mostly unclear. In conclusion, the high prevalence of ear and hearing anomalies in patients with craniofacial microsomia underlines the importance of audiological screening in order to facilitate individual treatment.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.316
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations22
Published2022
Admission routes1
Has abstractyes

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