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The Importance of Mask Selection on Continuous Positive Airway Pressure Outcomes for Obstructive Sleep Apnea. An Official American Thoracic Society Workshop Report

2020· article· en· W3090664819 on OpenAlexaff
Pedro R. Genta, Marta Kamińska, Bradley A. Edwards, Matthew R. Ebben, Ana C. Krieger, Renaud Tamisier, Lichuan Ye, Terri E. Weaver, Olivier M. Vanderveken, Geraldo Lorenzi‐Filho, Pam DeYoung, William Hevener, Patrick J. Strollo

Bibliographic record

VenueAnnals of the American Thoracic Society · 2020
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsCanadian Sleep & Circadian Network
FundersMedical Center, University of PittsburghFaculty of Medicine and Health, University of SydneyUniversity of Illinois at Urbana-ChampaignUniversiteit AntwerpenUniversity of Pittsburgh
KeywordsMedicineContinuous positive airway pressureObstructive sleep apneaSleep apneaSelection (genetic algorithm)Positive airway pressureIntensive care medicineSleep (system call)Cardiology

Abstract

fetched live from OpenAlex

Continuous positive airway pressure (CPAP) remains the major treatment option for obstructive sleep apnea (OSA). The American Thoracic Society organized a workshop to discuss the importance of mask selection for OSA treatment with CPAP. In this workshop report, we summarize available evidence about the breathing route during nasal and oronasal CPAP and the importance of nasal symptoms for CPAP outcomes. We explore the mechanisms of air leaks during CPAP treatment and possible alternatives for leak control. The impact of nasal and oronasal CPAP on adherence, residual apnea-hypopnea index, unintentional leaks, and pressure requirements are also compared. Finally, recommendations for patient and partner involvement in mask selection are presented, and future directions to promote personalized mask selection are 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 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)0.021
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.409
Teacher spread0.346 · 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 designNot applicable
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

Citations84
Published2020
Admission routes1
Has abstractyes

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