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Record W2990020467 · doi:10.1038/s41598-019-54375-9

Hyperventilation-induced heart rate response as a potential marker for cardiovascular disease

2019· article· en· W2990020467 on OpenAlexaff
Selwynne M. Hawkins, Dominik P. Guensch, Matthias G. Friedrich, Giulia Vinco, Gobinath Nadeshalingham, Michel White, François‐Pierre Mongeon, Elizabeth Hillier, Tiago Teixeira, Jacqueline Flewitt, Balthasar Eberle, Kady Fischer

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsLibin Cardiovascular Institute of AlbertaMontreal Heart InstituteMcGill UniversityUniversity of CalgaryMcGill University Health Centre
Fundersnot available
KeywordsHyperventilationMedicineCoronary artery diseaseHeart rateCardiologyInternal medicineHemodynamicsHeart failureHeart rate variabilityDiseaseBlood pressure

Abstract

fetched live from OpenAlex

Abstract An increase of heart rate to physical or mental stress reflects the ability of the autonomous nervous system and the heart to respond adequately. Hyperventilation is a user-controlled breathing maneuver that has a significant impact on coronary function and hemodynamics. Thus, we aimed to investigate if the heart rate response to hyperventilation (HRR HV ) can provide clinically useful information. A pooled analysis of the HRR HV after 60 s of hyperventilation was conducted in 282 participants including healthy controls; patients with heart failure (HF); coronary artery disease (CAD); a combination of both; or patients suspected of CAD but with a normal angiogram. Hyperventilation significantly increased heart rate in all groups, although healthy controls aged 55 years and older (15 ± 9 bpm) had a larger HRR HV than each of the disease groups (HF: 6 ± 6, CAD: 8 ± 8, CAD+/HF+: 6 ± 4, and CAD−/HF−: 8 ± 6 bpm, p < 0.001). No significant differences were found between disease groups. The HRR HV may serve as an easily measurable additional marker of cardiovascular health. Future studies should test its diagnostic potential as a simple, inexpensive pre-screening test to improve patient selection for other diagnostic exams.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0010.000

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 head0.016
GPT teacher head0.259
Teacher spread0.244 · 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 designBench or experimental
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

Citations18
Published2019
Admission routes1
Has abstractyes

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