MétaCan
Menu
Back to cohort
Record W2991053574 · doi:10.3390/jcm8122120

Transition to Adult Care for Obstructive Sleep Apnea

2019· review· en· W2991053574 on OpenAlexafffund
Austin Heffernan, Uzair Malik, Russell Cheng, Shaun Yo, Indra Narang, Clodagh M. Ryan

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
FundersPhysicians' Services Incorporated Foundation
KeywordsMedicineObstructive sleep apneaSleep apneaObesityGuidelinePediatricsSleep (system call)Young adultIntensive care medicineApneaGerontologyPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Obstructive sleep apnea may occur throughout the lifespan, with peak occurrences in early childhood and during middle and older age. Onset in childhood is overwhelmingly due to adeno-tonsillar hypertrophy, while in adulthood, contributors include risk factors, such as obesity, male sex, and aging. More recently, there has been a precipitous increase in the prevalence of obstructive sleep apnea in youth. Drivers of this phenomenon include both increasing obesity and the survival of children with complex medical conditions into adulthood. Appropriate treatment and long-term management of obstructive sleep apnea is critical to ensure that these youth maintain well-being unfettered by secondary comorbidities. To this end, patient engagement and seamless transition of care from pediatric to adult health care systems is of paramount importance. To date, this is an unacknowledged and unmet need in most sleep programs. This article highlights the need for guideline-driven sleep disorder transition processes and illustrates the authors' experience with the development of a program for sleep apnea.

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)0.001
metaresearch head score (Gemma)0.004
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: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.333
GPT teacher head0.619
Teacher spread0.286 · 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

Citations9
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical MedicineSame topicAdolescent and Pediatric HealthcareFrench-language works237,207