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Record W2094084842 · doi:10.1055/s-2005-863789

Using a Telehealth Medium for Objective Hearing Testing: Implications for Supporting Rural Universal Newborn Hearing Screening Programs

2005· article· en· W2094084842 on OpenAlexaff
Mark Krumm, John Ribera, Jason Schmiedge

Bibliographic record

VenueSeminars in Hearing · 2005
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsKelowna General Hospital
FundersUniversity of UtahUtah State University
KeywordsTelehealthTelemedicineHearing lossMedicineAsynchronous communicationTelerehabilitationRural areaAudiologyMedical educationNursingHealth careComputer scienceTelecommunicationsPolitical sciencePathology

Abstract

fetched live from OpenAlex

Telepractice is commonly used in other health professions to dispense a large number of services. These services include diagnostics, rehabilitation, and counseling. Telepractice is increasingly available to practitioners serving rural areas and can be modified for many health applications. Yet telepractice is not widely used by audiologists. This is somewhat surprising because audiology seems poised to offer telepractice using both synchronous and asynchronous technology. Furthermore, telepractice may be particularly effective for enhancing a universal newborn hearing screening (UNHS) program in rural areas. Specifically, rural communities often lack the proper personnel and program continuity to serve newborns with hearing loss effectively. Telepractice may be a method that can ameliorate these problems. Presently, Utah State University is evaluating the value of telepractice for UNHS services. Initial data obtained from this project are promising and support the validity of telepractice with infant hearing services. However, research with a much larger group of subjects will be required before telepractice can be used with confidence in a UNHS program.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.666
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.139
GPT teacher head0.382
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.

Study designSimulation or modeling
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

Citations34
Published2005
Admission routes1
Has abstractyes

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