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Record W3043171384 · doi:10.15331/jdsm.7134

Continuous tongue suction as a potential therapy for obstructive sleep apnea: A feasibility study

2020· article· en· W3043171384 on OpenAlexaff
Tatsuya Fukuda, Yoichiro Takei, Hideaki Nakayama, Yuichi Inoue, Satoru Tsuiki

Bibliographic record

VenueJournal of Dental Sleep Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia
FundersJapan Society for the Promotion of Science
KeywordsObstructive sleep apneaTongueMedicineSuctionSleep (system call)Sleep apneaOral applianceAnesthesiaIntensive care medicineComputer scienceEngineeringPathologyMechanical engineering

Abstract

fetched live from OpenAlex

During wakefulness, apneic events, even in patients with severe obstructive sleep apnea (OSA), rarely occur regardless of the presence or absence of such episodes while asleep, because the augmented activity of the genioglossus muscle acts to patent the upper airway by maintaining the tongue in position.Hence, it is reasonable to hypothesize that OSA could be alleviated if the awake tongue position is maintained despite a sleep-related reduction in genioglossus muscle activity.The median (interquartile range) respiratory event index was significantly reduced with continuous tongue suction (23 [16-27] to 8 [7-14] events/h, P = 0.043) in 5 patients who successfully completed the protocol.Because this approach does not require either positive airway pressure or mandibular advancement, it makes it possible to completely avoid the adverse effects associated with the use of nasal continuous positive airway pressure and/or mandibular advancement devices in patients for whom nasal continuous positive airway pressure and mandibular advancement devices are contraindicated.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.344
Teacher spread0.310 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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
Published2020
Admission routes1
Has abstractyes

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