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Record W2027591164 · doi:10.1155/2012/284864

Auditory Brainstem Responses to Bone-Conducted Brief Tones in Young Children with Conductive or Sensorineural Hearing Loss

2012· article· en· W2027591164 on OpenAlexaff
Jennifer L. Hatton, Renée M. Janssen, David R. Stapells

Bibliographic record

VenueInternational Journal of Otolaryngology · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing, Cochlea, Tinnitus, Genetics
Canadian institutionsBC Children's HospitalProvincial Health Services AuthorityUniversity of British Columbia
Fundersnot available
KeywordsAudiologyMedicineSensorineural hearing lossAuditory brainstem responseConductive hearing lossHearing lossBone conduction

Abstract

fetched live from OpenAlex

The bone-conduction (BC) tone ABR has been used clinically for over 20 years. The current study formally evaluated the test performance of the BC tone-evoked ABR in infants with hearing loss. Method. By comparing BC-ABR results to follow-up behavioural results, this study addressed two questions: (i) whether the BC tone ABR was successful in differentiating children with conductive versus sensorineural hearing loss (Study A; conductive: 68 ears; SNHL: 129 ears) and (ii) the relationship between BC ABR and behavioural hearing loss severity (Study B: 2000 Hz: 104 ears; 500 Hz: 47 ears). Results. Results demonstrate that the "normal" BC-ABR levels accurately differentiated normal versus elevated cochlear sensitivity (accuracy: 98% for 2000 Hz; 98% for 500 Hz). A subset of infants in Study A with elevated BC-ABR (i.e., no response at normal level) had additional testing at higher intensities, which allowed for categorization of the degree of cochlear impairment. Study B results indicate that the BC ABR accurately categorizes the degree of cochlear hearing loss for 2000 Hz (accuracy = 95.2%). A preliminary dBnHL-to-dBHL correction factor of "0 dB" was determined for 2000 Hz BC ABR. Conclusions. These findings further support the use of BC tone ABR for diagnostic ABR testing.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.327
Teacher spread0.277 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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