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Record W36069121 · doi:10.1055/s-0042-1748049

Preferred Listening Levels of Children Who Use Hearing Aids: Comparison to Prescriptive Targets

2000· article· en· W36069121 on OpenAlexaff
Susan Scollie, Richard C. Seewald, Kristina Moodie, Kate Dekok

Bibliographic record

VenueJournal of the American Academy of Audiology · 2000
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsWestern University
Fundersnot available
KeywordsLoudnessAudiologyDigital subscriber lineActive listeningSensorineural hearing lossHearing lossSound pressurePsychologyMedicineComputer scienceTelecommunicationsCommunication

Abstract

fetched live from OpenAlex

Abstract The preferred listening levels (PLLs) of children with sensorineural hearing loss were elicited using conversation-level speech, heard through the children's own hearing aids. All hearing aids were fitted using the desired sensation level (DSL) method. Comparisons were made between the PLL and targets from the following prescriptive formulae: DSL version 4.1 and two versions of the National Acoustic Laboratories (NAL) procedure, including NAL revised for severe-profound losses (NAL)-RP and NAL nonlinear NAL/NL1. Results for this sample of children indicated that the PLL was similar to the DSL targets, and that, on average, NAL-RP/NL1 targets recommended less gain than that preferred by the majority of children in this study. The implications of factors such as acclimatization, test levels, and clinical procedures on these results are discussed. Abbreviations: ANOVA = analysis of variance, BLS = binaural loudness summation, BTE = behind the ear, DSL = desired sensation level, LDL = loudness discomfort level, MCL = most comfortable level, NAL = National Acoustics Laboratories, PLL = preferred listening level, POGO = prescription of gain and output, RECD = real-ear-to-coupler difference, SPL = sound pressure level, SSPL = saturation sound pressure level, WDRC = wide dynamic range compression

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.069
GPT teacher head0.342
Teacher spread0.273 · 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

Citations35
Published2000
Admission routes1
Has abstractyes

Explore more

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