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Record W4386820517 · doi:10.1080/14992027.2023.2254934

Validation of an integrated pressure level measured earmold wideband real-ear-to-coupler difference measurement

2023· article· en· W4386820517 on OpenAlexaff
Matthew Urichuk, David W. Purcell, Prudence Allen, Susan Scollie

Bibliographic record

VenueInternational Journal of Audiology · 2023
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsWestern University
Fundersnot available
KeywordsHearing aidMicrophoneAcousticsAudiologySound pressureWidebandMedicinePhysicsOptics

Abstract

fetched live from OpenAlex

OBJECTIVE: To validate measurement of predicted earmold wideband real-ear-to-coupler difference (wRECD) using an integrated pressure level (IPL) calibrated transducer and the incorporation of an acoustically measured tubing length correction. DESIGN: Unilateral earmold SPL wRECD using varied hearing aid tubing length and the proposed predicted earmold IPL wRECD measurement procedure were completed on all participants and compared. STUDY SAMPLE: 22 normal hearing adults with normal middle ear status were recruited. RESULTS: There were no clinically significant differences between probe-microphone and predicted earmold IPL wRECD measurements between 500 and 2500 Hz. Above 5000 Hz, the predicted earmold IPL wRECD exceeded earmold SPL wRECDs due to lack of standing wave interference. Test-retest reliability of IPL wRECD measurement exceeded the reliability of earmold SPL wRECD measurement across all assessed frequencies, with the greatest improvements in the high frequencies. The acoustically measured tubing length correction largely accounted for acoustic effects of the participant's earmold. CONCLUSIONS: IPL-based measurements provide a promising alternative to probe-microphone earmold wRECD procedures. Predicted earmold IPL wRECD is measured without probe-microphone placement, agrees well with earmold SPL wRECDs and is expected to extend the valid bandwidth of wRECD measurement.

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)0.001
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 score0.438
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.087
GPT teacher head0.323
Teacher spread0.236 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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