MétaCan
Menu
Back to cohort
Record W4407383821 · doi:10.1111/adj.13058

Myofunctional therapy for obstructive sleep apnoea

2024· review· en· W4407383821 on OpenAlex
W Li, Frédéric Sériès

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAustralian Dental Journal · 2024
Typereview
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité Laval
Fundersnot available
KeywordsMedicinePhysical therapyAirwayDilatorAmbulatoryPopulationPolysomnographyDeconditioningPhysical medicine and rehabilitationApneaAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Failure of upper airway muscles to develop efficient dilating forces plays a key role in the occurrence of obstructive sleep apnoea in given patients. Thus, myofunctional therapy has been developed to improve the activity/efficacy of the upper airway (UA) dilator muscles, reduce its fatigability and improve mechanical performance. Various programmes, differing in the types of daytime exercises to be completed, as well as in their duration and intensity, have been evaluated. Meta-analysis confirmed the efficacy of myofunctional therapy, with mean apnoea hypopnoea index (AHI) scores decreasing from 28.0 ± 16.2/h to 18.6 ± 13.1/h, and lowest oxygen saturation (LSAT) values improving from 83.2% ± 6.1% to 85.1% ± 7.0%. In children, MT and nasal washing may result in little to no difference in AHI. Integrating oropharyngeal exercises with the use of a smartphone application to complete and record exercise performances represents an innovative turn in the development of ambulatory MT programmes. Since adherence to therapy is a weakness in conventional OSA strategies such as CPAP, this approach to MT is promising, as evidenced by a 90% mean adherence to it after 3 months of using a smart application. There is further need to determine the most effective combination of exercise algorithms and identify the target population most likely to benefit from MT in outpatient training programmes.

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.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.104
GPT teacher head0.422
Teacher spread0.318 · 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