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Record W2082196577 · doi:10.1139/h07-043

Hyperthermia and voluntary exhaustion: integrating models and future challenges

2007· review· en· W2082196577 on OpenAlexafffundvenue
Stephen S. Cheung

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2007
Typereview
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWorkloadHyperthermiaPerspective (graphical)LimitingHeat illnessHeat stressThermoregulationTurnoverPsychologyCognitive psychologyMedicineComputer scienceEconomicsEngineeringMechanical engineeringBiology

Abstract

fetched live from OpenAlex

Over the past decade, research interest has risen on the direct effects of temperature on exercise capacity and tolerance, particular in the heat. Two major paradigms have been proposed for how hyperthermia may contribute to voluntary fatigue during exercise in the heat. One suggests that voluntary exhaustion occurs upon the approach or attainment of a critical internal temperature through impairment in a variety of physiological systems. An alternate perspective proposes that thermal inputs modulate the regulation of self-paced workload to minimize heat storage. This review seeks to summarize recent research leading to the development of these two models for hyperthermia and fatigue and explore possible bridges between them. Key areas for future research and development into voluntary exhaustion in the heat include (i) the development of valid and non-invasive means to measure brain temperature, (ii) understanding variability in perception and physiological responses to heat stress across individuals, (iii) extrapolating laboratory studies to field settings, (iv) understanding the failure in behavioural and physiological thermoregulation that leads to exertional heat illness, and (v) the integration of physiological and psychological parameters limiting voluntary exercise in the heat.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.083
GPT teacher head0.337
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations59
Published2007
Admission routes3
Has abstractyes

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