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Record W2727896988 · doi:10.1097/mao.0000000000001490

Diagnosis of Superior Semicircular Canal Dehiscence in the Presence of Concomitant Otosclerosis

2017· article· en· W2727896988 on OpenAlexaff
Michael Yong, Erica Helena Zaia, Brian D. Westerberg, Jane Lea

Bibliographic record

VenueOtology & Neurotology · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineOtosclerosisDehiscenceConductive hearing lossSemicircular canalSurgeryHearing lossAudiometryPhysical examinationMiddle earRadiologyAudiologyVestibular system

Abstract

fetched live from OpenAlex

OBJECTIVE: To review three patients with concurrent otosclerosis and superior canal dehiscence identified before operative intervention and provide a practical diagnostic approach to this clinical scenario. STUDY DESIGN: Retrospective patient series. SETTING: Tertiary/quaternary referral center. PATIENTS: Individuals with confirmed diagnoses of concurrent otosclerosis and superior semicircular canal dehiscence syndrome. INTERVENTIONS: Detailed history and physical examinations were performed on these patients, as well as detailed audiovestibular testing and computed tomography imaging. MAIN OUTCOME MEASURES: Establishing a clear diagnosis of concurrent otosclerosis and superior semicircular canal dehiscence syndrome using a thorough diagnostic approach. RESULTS: Three patients presented with conductive hearing loss and normal tympanic membranes. When history and physical examination yielded suspicious third window symptoms/signs, more detailed audiovestibular testing and computed tomography scan imaging were performed. All three patients were ultimately identified to have concurrent otosclerosis and superior canal dehiscence. Conservative management was the option of choice for two of these patients (trial of a hearing aid) and surgical intervention was performed to treat the otosclerosis in the remaining patient.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.146
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.040
GPT teacher head0.288
Teacher spread0.249 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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