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Knowledge Gaps in the Perioperative Management of Adults with Obstructive Sleep Apnea and Obesity Hypoventilation Syndrome. An Official American Thoracic Society Workshop Report

2018· article· en· W2786168480 on OpenAlexaff
Najib Ayas, Cheryl R. Laratta, John M. Coleman, Anthony G. Doufas, Matthias Eikermann, Peter C. Gay, Daniel J. Gottlieb, Indira Gurubhagavatula, David R. Hillman, Roop Kaw, Anil K. Malhotra, Babak Mokhlesi, Timothy I. Morgenthaler, Sairam Parthasarathy, Satya Krishna Ramachandran, Kingman P. Strohl, Patrick J. Strollo, Michael J. Twery, Phyllis C. Zee, Frances Chung

Bibliographic record

VenueAnnals of the American Thoracic Society · 2018
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia Hospital
FundersNational Heart, Lung, and Blood InstituteSociety of Anesthesia and Sleep Medicine
KeywordsMedicineObesity hypoventilation syndromeObstructive sleep apneaHypoventilationObesitySleep medicineSleep apneaPerioperativeIntensive care medicineInternal medicineAnesthesiaRespiratory systemPsychiatrySleep disorderInsomnia

Abstract

fetched live from OpenAlex

The purpose of this workshop was to identify knowledge gaps in the perioperative management of obstructive sleep apnea (OSA) and obesity hypoventilation syndrome (OHS). A single-day meeting was held at the American Thoracic Society Conference in May, 2016, with representation from many specialties, including anesthesiology, perioperative medicine, sleep, and respiratory medicine. Further research is urgently needed as we look to improve health outcomes for these patients and reduce health care costs. There is currently insufficient evidence to guide screening and optimization of OSA and OHS in the perioperative setting to achieve these objectives. Patients who are at greatest risk of respiratory or cardiac complications related to OSA and OHS are not well defined, and the effectiveness of monitoring and other interventions remains to be determined. Centers involved in sleep research need to develop collaborative networks to allow multicenter studies to address the knowledge gaps identified below.

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)0.035
metaresearch head score (Gemma)0.051
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: Review · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0040.007
Open science0.0020.006
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.399
Teacher spread0.344 · 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
GenreReview

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

Citations29
Published2018
Admission routes1
Has abstractyes

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