MétaCan
Menu
Back to cohort
Record W2017662706 · doi:10.1109/embc.2012.6346799

Sleep-wake and circadian-dependent variation of cardiorespiratory coherence

2012· article· en· W2017662706 on OpenAlexafffund
Philippe Boudreau, Chris J. Brouse, Guy A. Dumont, Diane B. Boivin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of British ColumbiaMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsCircadian rhythmWakefulnessUltradian rhythmMorningCardiorespiratory fitnessFree-running sleepSleep (system call)Internal medicineMedicineHeart rate variabilityAutonomic nervous systemArousalEndocrinologyPsychologyHeart rateNeuroscienceLight effects on circadian rhythmSuprachiasmatic nucleusElectroencephalographyBlood pressure

Abstract

fetched live from OpenAlex

The risk of adverse cardiovascular events is elevated in the morning compared to the rest of the day. A circadian and a sleep-wake dependent variation in the regulation of the cardiovascular system could contribute to this increased cardiovascular risk. Using an ultradian sleep-wake cycle (USW) procedure, our aim was to explore the effects of the sleep-wake and circadian cycles on cardiorespiratory coherence (CRC) as a measure of autonomic nervous system (ANS) state. Our results suggest a shift toward parasympathetic dominance with deepening of sleep. Conversely, REM sleep is associated with a sympathetic dominance comparable to levels observed during wakefulness. A circadian rhythm was observed for CRC during wakefulness and all sleep stages. Maximal sympathetic dominance was observed in the morning, as measured by CRC during wakefulness and REM sleep, consistent with studies showing increased cardiac risk in the morning. This study provides evidence that circadian and sleep processes interact to influence the ANS modulation of the heart.

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 score0.520
Threshold uncertainty score0.369

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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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

Citations7
Published2012
Admission routes2
Has abstractyes

Explore more

Same topicHeart Rate Variability and Autonomic ControlFrench-language works237,207