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Record W2168492844 · doi:10.1152/ajpregu.00546.2009

Estimating changes in volume-weighted mean body temperature using thermometry with an individualized correction factor

2010· article· en· W2168492844 on OpenAlexaff
Ollie Jay, Michel B. Ducharme, Paul W. Webb, Francis D. Reardon, Glen P. Kenny

Bibliographic record

VenueAmerican Journal of Physiology-Regulatory, Integrative and Comparative Physiology · 2010
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsDefence Research and Development CanadaUniversity of Ottawa
Fundersnot available
KeywordsLinear regressionBody mass indexAnimal scienceCalorimetryBody surface areaCore (optical fiber)Relative humidityChemistryRectal temperatureStepwise regressionHeat indexMathematicsAnalytical Chemistry (journal)Internal medicineStatisticsMaterials scienceMedicineBiologyThermodynamicsPhysicsChromatographyHeat stress

Abstract

fetched live from OpenAlex

This study investigated whether the estimation error of volume-weighted mean body temperature (DeltaT(b)) using changes in core and skin temperature can be accounted for using personal and environmental parameters. Whole body calorimetry was used to directly measure DeltaT(b) in an Experimental group (EG) of 36 participants (24 males, 12 females) and a Validation group (VG) of 20 (9 males, 11 females) throughout 90 min of cycle ergometry at 40 degrees C, 30% relative humidity (RH) (n = 9 EG, 5 VG); 30 degrees C, 30% RH (n = 9 EG, 5 VG); 30 degrees C, 60% RH (n = 9 EG, 5 VG); and 24 degrees C, 30% RH (n = 9 EG, 5 VG). The core of the two-compartment thermometry model was represented by rectal temperature and the shell by a 12-point mean skin temperature (DeltaT(sk)). The estimation error (X(0)) between DeltaT(b) from calorimetry and DeltaT(b) from thermometry using core/shell weightings of 0.66/0.34, 0.79/0.21, and 0.90/0.10 was calculated after 30, 60, and 90 min of exercise, respectively. The association between X(0) and the individual variation in metabolic heat production (M - W), body surface area (BSA), body fat percentage (%fat), and body surface area-to-mass ratio (BSA/BM) as well as differences in environmental conditions (Oxford index) in the EG data were assessed using stepwise linear regression. At all time points and with all core/shell weightings tested, M - W, BSA, and Oxford index independently correlated significantly with the residual variance in X(0), but %fat and BSA/BM did not. The subsequent regression models were used to predict the thermometric estimation error (X(0_pred)) for each individual in the VG. The value estimated for X(0_pred) was then added to the DeltaT(b) estimated using the two-compartment thermometry models yielding an adjusted estimation (DeltaT(b)_(adj)) for the individuals in the VG. When comparing DeltaT(b)_(adj) to the DeltaT(b) derived from calorimetry in the VG, the best performing model used a core/shell weighting of 0.66/0.34 describing 74%, 84%, and 82% of the variation observed in DeltaT(b) from calorimetry after 30, 60, and 90 min, respectively.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.030
GPT teacher head0.329
Teacher spread0.299 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations11
Published2010
Admission routes1
Has abstractyes

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