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Predicting Cardiovascular Hemodynamics in Cold and Warm Environments at Rest with Skin and Core Temperature

2018· article· en· W3175222816 on OpenAlexaff
Nicholas Beckett-Brown, Sandra C. Dorman, Thomas Merritt, Dominique D. Gagnon

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsLaurentian University
Fundersnot available
KeywordsHemodynamicsMedicineThermoregulationLean body massLinear regressionCardiologyBlood flowSkin temperatureCore (optical fiber)Cardiorespiratory fitnessInternal medicineBody weightBiomedical engineeringMathematicsMaterials science

Abstract

fetched live from OpenAlex

Many occupations require workers to be exposed to extreme heat or cold. During prolonged exposure to extreme environments, the cardiovascular system plays a critical role in thermal homeostasis, by adjusting blood flow in peripheral and central vascular beds. Importantly, exposure to either environment, and the concordant blood‐volume redistribution, increases morbidity and mortality from the additional [and often significant] stress on the cardiovascular system. To gain insight on how to predict cardiovascular stress during exposure to extreme environments at rest, this study attempted to build a regression model to examine the influence of progressive, whole‐body skin and core cooling and heating on cardiovascular hemodynamics. Seventeen participants (12 males, 22.9 ± 3.58 years, 174.2 cm ± 7.85 cm, 12.8 ± 6.20 kg fat mass, and 59.3 ± 9.49 kg fat free mass) wearing shorts and a t‐shirt participated in two laboratory sessions: i) progressive heating (22°C to 40°C); and ii) progressive cooling (22°C to 5°C); each aimed to independently influence skin and core temperatures. Cardiovascular hemodynamics data was collected via thoracic electrical bioimpedance, while metabolic and cardiorespiratory variables were assessed via indirect calorimetry. Mean weighted skin (T sk ) and core (T c ) temperature were measured via portable data loggers and a thermocouple, respectively. Mean weighted skin temperatures ranged from 22.7°C to 37.4°C and T c ranged from 35.5°C to 37.9°C. A stepwise multiple linear regression analysis was conducted using T sk , T c , fat free mass, fat mass, sex and age as predictors for cardiovascular hemodynamics changes under thermal stress. The present model explained 24.4% of changes in stroke volume, 27.9% of heart rate, 15.9% of cardiac output and 49.5% of oxygen consumption. Although changes in T sk and T c temperature induced by whole‐body cooling and heating at rest accounted for changes in cardiovascular hemodynamics in the model, a significant portion of the variation remained unexplained. Further research is needed to develop a more accurate model to represent changes in cardiovascular hemodynamics under thermal stress. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
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 head0.021
GPT teacher head0.237
Teacher spread0.217 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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