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Record W2044112814 · doi:10.3766/jaaa.22.6.6

Adaptation of the BKB-SIN Test for Use as a Pediatric Aided Outcome Measure

2011· article· en· W2044112814 on OpenAlexaff
Stella Ng, Susan Scollie, Richard C. Seewald

Bibliographic record

VenueJournal of the American Academy of Audiology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsHearing aidAudiologyMedicineHearing lossTest (biology)

Abstract

fetched live from OpenAlex

BACKGROUND: There is a need for objective pediatric hearing aid outcome measurement and thus a need for the evaluation of outcome measures. We explored a commercially available pediatric sentence-in-noise measure adapted for use as an aided outcome measure. PURPOSE: The purposes of the current study were (1) to administer an adapted BKB-SIN (Bamford-Kowal-Bench Speech-in-Noise test) to adults and children who have normal hearing and children who use hearing aids and (2) to evaluate the utility of this adapted BKB-SIN as an aided, within-subjects outcome measure for amplification strategies. RESEARCH DESIGN: We used a mixed within and between groups design to evaluate speech recognition in noise for the three groups of participants. The children who use hearing aids were tested under the omnidirectional, directional, and digital noise reduction (DNR) conditions. Results from each group were compared to each other, and we compared results of each aided condition for the children who use hearing aids to evaluate the test utility as an aided outcome measure. STUDY SAMPLE: The study sample consisted of 14 adults with normal hearing (aged 22-28 yr) and 15 children with normal hearing (aged 6-18 yr), recruited through word of mouth, and 14 children who use hearing aids (aged 9-16 yr) recruited from local audiology clinics. DATA COLLECTION AND ANALYSIS: List pairs of the BKB-SIN test were presented at 50 dB HL as follows: four list pairs to each participant with normal hearing, four list pairs in the omnidirectional condition, and two list pairs in the directional and DNR conditions. Children who use hearing aids were fitted bilaterally with laboratory devices and completed the BKB-SIN test aided. Data were plotted as mean percent of key words correct at each signal-to-noise ratio (SNR). Further, we conducted an analysis of variance for group differences and within-groups for the three aided conditions. RESULTS: Adult participants outperformed children with normal hearing, who outperformed the children who use hearing aids. SNR-50 (signal-to-noise ratio at which listener can obtain a speech recognition score of 50% correct) scores demonstrated reliability of the adapted test implementation. The BKB-SIN test measured significant differences in performance for omnidirectional versus directional microphone conditions but not between omnidirectional and DNR conditions. CONCLUSIONS: We conclude that the adapted implementation of the BKB-SIN test can be administered reliably and feasibly. Further study is warranted to develop norms for the adapted implementation as well as to determine if an adapted implementation can be sensitive to age effects. Until such norms are developed, clinicians should refrain from comparing results from the adapted test to the test manual norms and should instead use the adapted implementation as a within-subject measure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.130
GPT teacher head0.340
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), 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
Published2011
Admission routes1
Has abstractyes

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