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Record W4399670433 · doi:10.1093/sleepadvances/zpae035

Oral appliance therapy and hypoglossal nerve stimulation as non-positive airway pressure treatment alternatives for obstructive sleep apnea: a narrative expert review

2024· article· en· W4399670433 on OpenAlexaff
Sairam Parthasarathy, Najib Ayas, Richard Bogan, Dennis Hwang, Clete A. Kushida, Jonathan S Lown, Joseph Ojile, Imran Patel, Bharati Prasad, David M. Rapoport, Patrick J. Strollo, Oliver M. Vanderveken, John S. Viviano

Bibliographic record

VenueSLEEP Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilNational Institutes of HealthSleep Research Society FoundationAmerican Academy of Sleep Medicine FoundationRegeneron PharmaceuticalsBrin Wojcicki FoundationSleep Research SocietyPatient-Centered Outcomes Research InstituteNational Heart, Lung, and Blood InstituteU.S. Department of Defense
KeywordsHypoglossal nerveObstructive sleep apneaOral applianceMedicineStimulationSleep (system call)Narrative reviewSleep apneaNarrativeTonguePhysical medicine and rehabilitationAnesthesiaIntensive care medicineComputer scienceInternal medicinePathologyArt

Abstract

fetched live from OpenAlex

This perspective on alternatives to positive airway pressure (PAP) therapy for the treatment of obstructive sleep apnea (OSA) summarizes the proceedings of a focus group that was conducted by the Sleep Research Society Foundation. This perspective is from a multidisciplinary panel of experts from sleep medicine, dental sleep medicine, and otolaryngology that aims to identify the current role of oral appliance therapy and hypoglossal nerve stimulation for the treatment of OSA with emphasis on the US practice arena. A secondary aim is to identify-from an implementation science standpoint-the various barriers and facilitators for adoption of non-PAP treatment that includes access to care, multidisciplinary expertise, reimbursement, regulatory aspects, current treatment guidelines, health policies, and other factors related to the delivery of care. The panel has contextualized the review with recent events-such as a large-scale PAP device recall compounded by supply chain woes of the pandemic-and emerging science in the field of OSA and offers solutions for multidisciplinary approaches while identifying knowledge gaps and future research opportunities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.372
Teacher spread0.345 · 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 designOther design
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

Citations1
Published2024
Admission routes1
Has abstractyes

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