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Record W4412658976 · doi:10.1080/14992027.2025.2537693

Validation of the clinical assistant for research and learning (CARL) for pure-tone audiometric procedures

2025· article· en· W4412658976 on OpenAlexaff
Mohamed Rahme, Muneeb Alam, Hasan K. Saleh, Paula Folkeard, Robert Koch, Steve Beaulac, Matthew Holden, Sheila Moodie, Vijay Parsa, Susan Scollie

Bibliographic record

VenueInternational Journal of Audiology · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsProcess Simulations Limited (Canada)Western University
Fundersnot available
KeywordsAudiologyTone (literature)Pure toneAudiometryPsychologyMedicineSpeech recognitionComputer scienceHearing lossLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the validity, clinical utility, barriers and facilitators of the manikin-based audiometric simulator, Clinical Assistant for Research and Learning. DESIGN: The validity, clinical utility, barriers, facilitators and simulation accuracy were evaluated via an online questionnaire and in-person pure-tone audiometric assessments. STUDY SAMPLE: A total of 38 participants (age range 21-63) completed the study. All participants had formal training in pure-tone audiometric assessments. Participants were audiology students, registered practicing audiologists, hearing instrument dispensers, and undergraduate students. RESULTS: The programmed and measured audiograms had an absolute agreement within 5 decibel hearing level. Ease of use, clarity of workflow, realism, and feasibility in clinical practice were the reported facilitators, whereas cost and availability of technical support were the identified barriers to implementation. CONCLUSIONS: The manikin-based simulator was shown to be an accurate and valid tool in practicing pure-tone audiometric procedures. Clinical applications and future directions were also discussed.

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)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
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 head0.169
GPT teacher head0.529
Teacher spread0.360 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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