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Record W2954136782 · doi:10.1016/j.heliyon.2019.e02034

Heart rate variability in pulmonary hypertension with and without sleep apnea

2019· article· en· W2954136782 on OpenAlexaff
Carolina Gonzaga, Richard Bresler, Ying Xuan Zhi, Hisham Alshaer, John Granton, Clodagh M. Ryan

Bibliographic record

VenueHeliyon · 2019
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsToronto General HospitalUniversity of TorontoToronto Rehabilitation InstituteUniversity Health Network
FundersPharmaceuticals BayerConselho Nacional de Desenvolvimento Científico e TecnológicoActelion PharmaceuticalsBayer
KeywordsMedicinePulmonary hypertensionPolysomnographyCardiologyInternal medicineObstructive sleep apneaHeart rate variabilitySleep apneaApneaApnea–hypopnea indexVentricleHeart rateBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVES: Our aims were to evaluate HRV in pulmonary hypertension (WHO Group 1 and 4) compared to control subjects, and to assess whether the presence of sleep apnea in those with pulmonary hypertension would be deleterious and cause greater impairment in HRV. METHODS: This retrospective case-control study analyzed electrocardiogram segments obtained from diagnostic polysomnography. RESULTS: Forty-one pulmonary hypertension patients were compared to 41 age, sex and apnea-hypopnea index matched healthy controls. The pulmonary hypertension group had decreased high frequency, very low frequency, low frequency, and percentage of normal R-R intervals that differ by > 50 ms compared to control subjects. Moderate to severe right ventricle dysfunction on echocardiography was a predictor of lower high frequency in pulmonary hypertension patients. CONCLUSIONS: There were no differences in any HRV measures in pulmonary hypertension patients with or without sleep apnea. Impaired HRV was demonstrated in pulmonary hypertension patients however, the presence of sleep apnea did not appear to further reduce vagal modulation.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.112
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.015
GPT teacher head0.267
Teacher spread0.251 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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