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Record W3091895395 · doi:10.1183/23120541.00141-2020

An evaluation of rural–urban disparities in treatment outcomes for obstructive sleep apnoea: study protocol for a prospective cohort study

2020· article· en· W3091895395 on OpenAlexafffund
Jennifer Corrigan, Imhokhai Ogah, Ada Ip, Heather Sharpe, Cheryl R. Laratta, Peter Peller, Willis H. Tsai, Sachin R. Pendharkar

Bibliographic record

VenueERJ Open Research · 2020
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsCalgary Laboratory ServicesUniversity of AlbertaUniversity of Calgary
FundersAlberta Health Services
KeywordsMedicinePsychological interventionContinuous positive airway pressureCohortProspective cohort studyResidenceQuality of life (healthcare)Cohort studyRural areaSocioeconomic statusPhysical therapyEmergency medicineIntensive care medicineEnvironmental healthObstructive sleep apneaPopulationDemographyInternal medicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Obstructive sleep apnoea (OSA) is a common and treatable chronic condition that is associated with significant morbidity and economic cost. Geography is increasingly being recognised as a barrier to diagnosis and treatment of many chronic diseases; however, no study to date has investigated the impact of place of residence on health outcomes in OSA. OBJECTIVE: rural settings. METHODS: A prospective cohort design will be used. Participants will be recruited through community-based CPAP providers and assigned to either the rural or urban cohort based on residential postal code. The primary outcome will be the difference in nightly hours of CPAP use between the two groups, measured 3 months after initiation of therapy. Secondary outcomes will include symptoms, quality of life, patient satisfaction and patient-borne costs. ANTICIPATED RESULTS: This study will determine whether there are differences in CPAP adherence or patient-reported outcomes between rural and urban patients with OSA. These results will highlight potential challenges with providing OSA care in rural populations and may inform health interventions to reduce urban-rural inequities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.241
GPT teacher head0.536
Teacher spread0.295 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations8
Published2020
Admission routes2
Has abstractyes

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