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Record W2603679398 · doi:10.1016/j.soard.2017.03.022

Perioperative management of obstructive sleep apnea in bariatric surgery: a consensus guideline

2017· article· en· W2603679398 on OpenAlexaff
Christel A.L. de Raaff, Marguerite Gorter-Stam, Nico de Vries, Ashish Sinha, H. Jaap Bonjer, Frances Chung, Usha K. Coblijn, Albert Dahan, Rick S. van den Helder, Antonius A. J. Hilgevoord, David R. Hillman, Michael Margarson, Samer G. Mattar, J. P. Mulier, Madeline J. L. Ravesloot, Beata M. M. Reiber, Anne-Sophie van Rijswijk, Preet Mohinder Singh, Roos Steenhuis, Mark Tenhagen, Olivier M. Vanderveken, Johan Verbraecken, David P. White, Nicole van der Wielen, Bart A. van Wagensveld

Bibliographic record

VenueSurgery for Obesity and Related Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineObstructive sleep apneaGuidelinePerioperativeExpert opinionContinuous positive airway pressurePolysomnographyIntensive care medicineMEDLINEGold standard (test)Delphi methodDelphiSurgeryApneaAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.006
metaresearch head score (Gemma)0.015
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: Methods · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0020.002

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 head0.021
GPT teacher head0.294
Teacher spread0.272 · 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
GenreMethods

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

Citations148
Published2017
Admission routes1
Has abstractno

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