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Record W1985909756 · doi:10.1017/s0265021508003931

Heat transfer coefficient

2008· article· en· W1985909756 on OpenAlexaff
M.J. English, Thomas M. Hemmerling

Bibliographic record

VenueEuropean Journal of Anaesthesiology · 2008
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsMcGill University
Fundersnot available
KeywordsHeat transfer coefficientMedicineHeat transferHeat fluxSkin temperatureImmersion (mathematics)ThermodynamicsComposite materialMaterials scienceBiomedical engineering

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: To improve heat transfer, the Medivance Arctic Sun Temperature Management System (Medivance, Inc., Louisville, CO, USA) features an adhesive, water-conditioned, highly conductive hydrogel pad for intimate skin contact. This study measured and compared the heat transfer coefficient (h), i.e. heat transfer efficiency, of this pad (hPAD), in a heated model and in nine volunteers' thighs; and of 10 degrees C water (hWATER) in 33 head-out immersions by 11 volunteers. METHODS: Volunteer studies had ethical approval and written informed consent. Calibrated heat flux transducers measured heat flux (W m-2). Temperature gradient (DeltaT) was measured between skin and pad or water temperatures. Temperature gradient was changed through the pad's water temperature controller or by skin cooling on immersion. RESULTS: The heat transfer coefficient is the slope of W m-2/DeltaT: its unit is W m-2 degrees C-1. Average with (95% CI) was: model, hPAD = 110.4 (107.8-113.1), R2 = 0.99, n = 45; volunteers, hPAD = 109.8 (95.5-124.1), R2 = 0.83, n = 51; and water immersion, hWATER = 107.1 (98.1-116), R2 = 0.86, n = 94. CONCLUSION: The heat transfer coefficient for the pad was the same in the model and volunteers, and equivalent to hWATER. Therefore, for the same DeltaT and heat transfer area, the Arctic Sun's heat transfer rate would equal water immersion. This has important implications for body cooling/rewarming rates.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.043
GPT teacher head0.262
Teacher spread0.219 · 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 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

Citations17
Published2008
Admission routes1
Has abstractyes

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