MétaCan
Menu
Back to cohort
Record W4367668761 · doi:10.1080/14992027.2023.2205009

Validity and reliability of integrated pressure level real-ear-to-coupler difference measurements

2023· article· en· W4367668761 on OpenAlexaff
Matthew Urichuk, David W. Purcell, Susan Scollie

Bibliographic record

VenueInternational Journal of Audiology · 2023
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsWestern University
Fundersnot available
KeywordsReliability (semiconductor)MicrophoneAudiologySound pressureEar canalAcousticsMathematicsPhysicsMedicine

Abstract

fetched live from OpenAlex

OBJECTIVES: (1) To validate the measurement of foam-tip real-ear-to-coupler differences (wRECD) using an integrated pressure level (IPL) method and (2) to compare the reliability of this method to SPL-based measurement of the wRECD. DESIGN: SPL-based wRECD and the proposed IPL wRECD measurement were completed bilaterally. Test-retest reliability of IPL wRECD was determined with full re-insertion into the ear canal and compared to published SPL wRECD test-retest data. STUDY SAMPLE: 22 adults with normal hearing and middle ear status were recruited. RESULTS: Differences between SPL-based wRECD and IPL wRECD measurements were within 1.51 dB on average below 5000 Hz. At and above 5000 Hz, IPL wRECD exceeded SPL wRECDs by 6.11 dB on average. The average test-retest difference for IPL wRECD across all assessed frequencies was 0.75 dB with the greatest improvements in reliability found below 750 Hz and above 3000 Hz. CONCLUSIONS: IPL wRECD yielded improved estimates compared to SPL wRECD in high frequencies, where standing-wave interference is present. Independence from standing wave interference resulted in increased wRECD values above 4000 Hz using the IPL measurement paradigm. IPL wRECD is more reliable than SPL wRECD, does not require precise probe-microphone placement, and provides a wider valid wRECD bandwidth than SPL-based 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)0.002
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 score0.022
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.103
GPT teacher head0.344
Teacher spread0.241 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of AudiologySame topicEar Surgery and Otitis MediaFrench-language works237,207