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Record W4323652598 · doi:10.1148/rg.220102

Cochlear Implantation: Systematic Approach to Preoperative Radiologic Evaluation

2023· article· en· W4323652598 on OpenAlexaff
Shivaprakash B. Hiremath, Asthik Biswas, Gopolang Mndebele, David Schramm, Birgit Ertl‐Wagner, Susan Blasér, Santanu Chakraborty

Bibliographic record

VenueRadiographics · 2023
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsOttawa HospitalUniversity of TorontoHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsMedicineSensorineural hearing lossCochlear implantationCochlear implantInner earTemporal boneAudiologyHearing lossCochlear nerveCochleaRehabilitationRadiologySurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Sensorineural hearing loss results from abnormalities that affect the hair cells of the membranous labyrinth, inner ear malformations, and conditions affecting the auditory pathway from the cochlear nerve to the processing centers of the brain. Cochlear implantation is increasingly being performed for hearing rehabilitation owing to expanding indications and a growing number of children and adults with sensorineural hearing loss. An adequate understanding of the temporal bone anatomy and diseases that affect the inner ear is paramount for alerting the operating surgeon about variants and imaging findings that can influence the surgical technique, affect the choice of cochlear implant and electrode type, and help avoid inadvertent complications. In this article, imaging protocols for sensorineural hearing loss and the normal inner ear anatomy are reviewed, with a brief description of cochlear implant devices and surgical techniques. In addition, congenital inner ear malformations and acquired causes of sensorineural hearing loss are discussed, with a focus on imaging findings that may affect surgical planning and outcomes. The anatomic factors and variations that are associated with surgical challenges and may predispose patients to periprocedural complications also are highlighted. © RSNA, 2023 Quiz questions for this article are available through the Online Learning Center. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. An earlier incorrect version of this article appeared online. This article was corrected on May 18, 2023.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.088
GPT teacher head0.333
Teacher spread0.245 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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