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Record W2609955532 · doi:10.1093/sleepj/zsx050.474

0475 COMPARISON OF PERIPHERAL ARTERIAL TONOMETRY AND POLYSOMNOGRAPHY FOR THE DIAGNOSIS OF OSA IN PATIENTS WITH CHRONIC OBSTRUCTIVE PULMONARY DISEASE

2017· article· en· W2609955532 on OpenAlexaff
Rachel Jen, Y Li, Pamela DeYoung, Erik Smales, J.E. Orr, David J. Moore, Atul Malhotra, O D Robert

Bibliographic record

VenueSLEEP · 2017
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePolysomnographyCOPDEpworth Sleepiness ScaleInternal medicinePulmonary function testingCardiologyPhysical therapyApnea

Abstract

fetched live from OpenAlex

Chronic obstructive pulmonary disease (COPD) is a prevalent disorder with high morbidity and mortality. Studies suggest that concomitant untreated sleep-disordered breathing (SDB) substantially worsens outcomes. In addition to cost and availability, PSG may be an unattractive option for SDB diagnosis given rapid oxygen desaturation with relatively little upper airway obstruction - and low inter-rater reliability of PSG in this patient group as a result. Thus, alternative diagnostic methods are needed. The WatchPAT (Itamar Medical) is a home sleep testing device which has been shown to be accurate for diagnosing SDB in normal population without significant lung disease. It is based on peripheral arterial tone (PAT), pulse rate, oxygen saturation, actigraphy, snoring recording, and body position. Previous WatchPAT studies excluded patients with COPD. We therefore sought to compare WatchPAT to PSG in detecting SDB in patient with COPD. 32 patients (19 men) previously diagnosed with COPD, aged 64 ± 7 years old, underwent simultaneous recording with full night in-lab PSG and WatchPAT. PSG scoring was performed according to Chicago criteria by a RPSGT (AHI-PSG), who was blinded to the automated scoring by WatchPAT software (AHI-WPAT). All COPD patients also completed pulmonary function tests, Pittsburgh Sleep Quality Index (PSQI), and Epworth Sleepiness Scale (ESS) questionnaires. Pearson correlation between AHI-PSG and AHI-WPAT was p =0.638, p ≤ 0.001; Pearson correlation between REM AHI-PSG and REM AHI and ODI by WatchPAT for all 32 patients were 0.824 and 0.869, p ≤ 0.001. Using a threshold of AHI ≥ 10, the sensitivity and specificity of WatchPAT for all 32 patient were 0.89 and 0.78, respectively. No significant correlations were found between AHI-PSG or AHI-WPAT with PSQI or ESS scores. These findings suggest that WatchPAT may be used to accurately detect SDB in patients with COPD, especially in those with REM predominant events. More studies using WatchPAT with underlying lung diseases will be required to determine if this can be used as a screening test for SDB in COPD patients, especially those who are at risk for hypoventilation, and what factors might cause discrepancy between WatchPAT with PSG. None

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
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 science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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