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Record W2734593847 · doi:10.1155/2017/8939461

Investigating Cost Implications of Incorporating Level III At-Home Testing into a Polysomnography Based Sleep Medicine Program Using Administrative Data

2017· article· en· W2734593847 on OpenAlexaffabout
Samuel A. Stewart, Erika Penz, Mark Fenton, Robert Skomro

Bibliographic record

VenueCanadian Respiratory Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of SaskatchewanDalhousie University
Fundersnot available
KeywordsMedicinePolysomnographySleep medicineSleep (system call)Physical therapyMEDLINESleep disorderInternal medicineInsomniaPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: Obstructive sleep apnea is a common problem, requiring expensive in-lab polysomnography for proper diagnosis. Home monitoring can provide an alternative to in-lab testing for a subset of OSA patients. The objective of this project was to investigate the effect of incorporating home testing into an OSA program at a large, tertiary sleep disorders centre. METHODS: The Sleep Disorders Centre in Saskatoon, Canada, has been incorporating at-home testing into their diagnostic pathways since 2006. Administrative data from 2007 to 2013 were extracted (10030 patients) and the flow of patients through the program was followed from diagnosis to treatment. Costs were estimated using 2014 pricing and were stratified by disease attributes and sensitivity analysis was applied. RESULTS: The overall costs per patient were $627.40, with $419.20 for at-home testing and $746.20 for in-lab testing. The cost of home management would rise to $515 if all negative tests were required to be confirmed by an in-lab PSG. DISCUSSION: Our review suggests that at-home testing can be cost-effective alternative to in-lab testing when applied to the correct population, specifically, those with a high pretest probability of obstructive sleep apnea and an absence of significant comorbidities.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.359
GPT teacher head0.436
Teacher spread0.077 · 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.

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

Citations16
Published2017
Admission routes2
Has abstractyes

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