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PW 2321 Human responses to five heated hypothermia enclosure systems in a cold environment

2018· article· en· W2889583105 on OpenAlexaffabout
Ramesh Dutta, Kartik Kulkarni, Phillip F. Gardiner, Alan M. Steinman, Gordon G. Giesbrecht

Bibliographic record

VenueAbstracts · 2018
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHypothermiaEnclosureShiveringCore temperatureSkin temperatureMedicineAnesthesiaEngineeringBiomedical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Objective The purpose of the study was to determine, and compare, the effectiveness of five heated hypothermia enclosure systems (HES). Methods This study compared the thermal, physiologic and subjective responses of five subjects (one female) in five HES (with vapor barrier and chemical heat sources) during 60 min of exposure to a −22°C climate. The five systems were: 1) user-assembled (Control); 2) Doctor Down® Rescue Wrap® (DD); 3) Hypothermia Prevention and Management Kit (HPMK®); 4) MARSARS Hypothermia Stabilizer Bag (M); and 5) Wiggy’s Victims Casualty Hypothermia Bag (W). Core temperature, skin heat loss, and metabolic heat production were determined continuously. Subjective responses were also evaluated for: whole body cold discomfort; overall shivering rating; temperature rating; and overall preferential ranking. Results Total heat loss was higher with HPMK, W and M compared to Control and DD (p<0.05). Net heat gain was higher with the Control and DD compared to W and M (p<0.05). Control, M and DD consistently scored better in the subjective scales. Conclusions Although all systems provide insulation and heat, the Control (user-assembled), MASARS and Doctor Down systems were more effective, and preferred. Funding NSERC, Canada.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.025
GPT teacher head0.299
Teacher spread0.274 · 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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