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Record W4380987888 · doi:10.3390/biology12060869

Resting Heart Rate Affects Heart Response to Cold-Water Face Immersion Associated with Apnea

2023· article· en· W4380987888 on OpenAlexaff
Krzysztof Malinowski, Tomasz H. Wierzba, J. Patrick Neary, Paweł J. Winklewski, Magdalena Wszędybył-Winklewska

Bibliographic record

VenueBiology · 2023
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsImmersion (mathematics)Heart rateRESTING HEART RATECardiac outputBiologyCold pressor testAnimal scienceBlood pressureEndocrinologyMathematics

Abstract

fetched live from OpenAlex

The regular cardiac response to immersion of the face in cold water is reduction in heart rate (HR). The highly individualized and unpredictable course of the cardiodepressive response prompted us to investigate the relationship between the cardiac response to face immersion and the resting HR. The research was conducted with 65 healthy volunteers (37 women and 28 men) with an average age of 21.13 years (20–27 years) and a BMI of 21.49 kg/m2 (16.60–28.98). The face-immersion test consisted of stopping breathing after maximum inhaling and voluntarily immersing the face in cold water (8–10 °C) for as long as possible. Measurements included determination of minimum, average, and maximum HR at rest and minimum and maximum HR during the cold-water face-immersion test. The results indicate a strong relationship between the cardiodepressive reaction of the immersion of the face and the minimum HR before the test, as well as a relationship between the maximum HR during the test and the maximum HR at rest. The results also indicate a strong influence of neurogenic HR regulation on the described relationships. The parameters of the basal HR can, therefore, be used as prognostic indicators of the course of the cardiac response of the immersion test.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.001

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 head0.024
GPT teacher head0.287
Teacher spread0.263 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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