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Record W4281726082 · doi:10.5770/cgj.25.527

Intelligibility of the Reversed-Stethoscope Technique in Age-Related Hearing Loss

2022· article· en· W4281726082 on OpenAlexaffvenue
Raphaelle Koerber, Linh Vaccarello, Allan Ho

Bibliographic record

VenueCanadian Geriatrics Journal · 2022
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of AlbertaAlberta Hospital EdmontonMcMaster University
Fundersnot available
KeywordsStethoscopeAudiologyIntelligibility (philosophy)MedicineHearing loss

Abstract

fetched live from OpenAlex

Background: This study evaluated the effectiveness of the reverse stethoscope technique in improving speech intelligibility. In this technique, a clinician places the earpieces of their stethoscope into the ears of a hearing-impaired patient and speaks into the chest piece. Methods: hearing instrument test box. The acoustic outputs of the stethoscopes and voice amplifier were measured across the frequency spectrum of speech. The Speech Intelligibility Index of the resulting speech was calculated for natural speech and for each device in relation to 10 standardized hearing losses representing the population of older adults. Results: For each of the 10 hearing losses, the speech signal emitted by the stethoscopes was quieter and yielded lower speech intelligibility scores than regular speech. In contrast, the voice amplifier provided mid- and high-frequency amplification and improved speech intelligibility scores for all but the mildest hearing losses. Conclusions: The reverse stethoscope technique worsens the clarity of speech and should not be used with older, hearing-impaired patients. Instead, clinicians should use regular speech or, preferably, personal voice amplifiers.

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)0.002
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 score0.611
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.273
Teacher spread0.243 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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