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Record W2156833299 · doi:10.1088/0967-3334/31/5/n01

The validity of tympanic and exhaled breath temperatures for core temperature measurement

2010· article· en· W2156833299 on OpenAlexafffund
Andreas D. Flouris, Stephen S. Cheung

Bibliographic record

VenuePhysiological Measurement · 2010
Typearticle
Languageen
FieldMedicine
TopicThermoregulation and physiological responses
Canadian institutionsBrock UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Space Agency
KeywordsCore temperatureCore (optical fiber)MedicineEnvironmental scienceBiomedical engineeringMaterials scienceComposite materialAnesthesia

Abstract

fetched live from OpenAlex

We examined the efficacy of tympanic (T(ty)) and exhaled breath (T(X)) temperatures as indices of rectal temperature (T(re)) by applying heat (condition A) and cold (condition B) in a dynamic A-B-A-B sequence. Fifteen healthy adults (8 men; 7 women; 24.9 +/- 4.6 years) volunteered. Following a 15 min baseline period, participants entered a water tank maintained at 42 degrees C water temperature and passively rested until their T(re) increased by 0.5 degrees C above baseline. Thereafter, they entered a different water tank maintained at 12 degrees C water temperature until their T(re) decreased by 0.5 degrees C below baseline. This procedure was repeated twice (i.e. A-B-A-B). T(ty) demonstrated moderate response delays to the repetitive changes in thermal balance, whereas T(X) and T(re) responded relatively fast. Both T(ty) and T(X) correlated significantly with T(re) (P < 0.05). Linear regression models were used to predict T(re) based on T(ty) and T(X). The predicted values from both models correlated significantly with T(re) (P < 0.05) and followed the changes in T(re) during the A-B-A-B thermal protocol. While some mean differences with T(re) were observed (P < 0.05), the 95% limits of agreement were acceptable for both models. It is concluded that the calculated models based on tympanic and exhaled breath temperature are valid indicators of core temperature.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.172
GPT teacher head0.330
Teacher spread0.159 · 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 designBench or experimental
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

Citations14
Published2010
Admission routes2
Has abstractyes

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