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Record W1982629133 · doi:10.1097/aud.0b013e31829c9670

Prediction of Conductive Hearing Loss Using Wideband Acoustic Immittance

2013· review· en· W1982629133 on OpenAlex
Beth A. Prieve, M. Patrick Feeney, Stefan Stenfelt, Navid Shahnaz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEar and Hearing · 2013
Typereview
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of British Columbia
FundersDefense Acquisition Program Administration
KeywordsTympanometryImmittanceAudiologyAcoustic reflexConductive hearing lossWidebandEar canalHearing lossMedicineMiddle earAudiometrySurgeryRadiology

Abstract

fetched live from OpenAlex

The purpose of this article was to review the effectiveness of wideband acoustic immittance (WAI) and tympanometry in detecting conductive hearing loss (CHL). Eight studies were included that measured CHL through air-and bone-conducted thresholds in at least a portion of their participants. One study included infants, three studies included children, one study included older children and adults, and three studies included adults. WAI identified CHL well in all populations. In infants and children, WAI in several single-frequency bands identified CHL with equal accuracy to measures of middle ear admittance using clinical tympanometry with a single probe tone (1000 Hz for infants; 226 Hz for children and adults). When WAI was combined across frequency bands, it identified CHL superior to traditional, single-frequency tympanometry. Only two studies used WAI tympanometry, which assesses the outer/middle ear across both frequency and introduced air pressure, and differing results were reported as to whether introducing pressure into the ear canal provides better identification of CHL. In general, WAI appears to be a promising clinical tool, and further investigation is warranted.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.172
GPT teacher head0.348
Teacher spread0.175 · 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