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Comparison of forced‐air warming systems with 
upper body blankets using a copper manikin of the human body

2002· article· en· W3103550622 on OpenAlexaff
Anselm Bräuer, M.J. English, N. Steinmetz, N. Lorenz, Thorsten Perl, U. Braun, W. Weyland

Bibliographic record

VenueActa Anaesthesiologica Scandinavica · 2002
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsBlanketMallinckrodtMedicineThermocoupleHeat fluxEnvironmental scienceThermoregulationHeat transferThermodynamicsMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Forced-air warming with upper body blankets has gained high acceptance as a measure for the prevention of intraoperative hypothermia. However, data on heat transfer with upper body blankets are not yet available. This study was conducted to determine the heat transfer efficacy of eight complete upper body warming systems and to gain more insight into the principles of forced-air warming. METHODS: Heat transfer of forced-air warmers can be described as follows: Qdot;=h. DeltaT. A, where Qdot;= heat flux [W], h=heat exchange coefficient [W m-2 degrees C-1], DeltaT=temperature gradient between the blanket and surface [ degrees C], and A=covered area [m2]. We tested eight different forced-air warming systems: (1) Bair Hugger and upper body blanket (Augustine Medical Inc. Eden Prairie, MN); (2) Thermacare and upper body blanket (Gaymar Industries, Orchard Park, NY); (3) Thermacare (Gaymar Industries) with reusable Optisan upper body blanket (Willy Rüsch AG, Kernen, Germany); (4) WarmAir and upper body blanket (Cincinnati Sub-Zero Products, Cincinnati, OH); (5) Warm-Gard and single use upper body blanket (Luis Gibeck AB, Upplands Väsby, Sweden); (6) Warm-Gard and reusable upper body blanket (Luis Gibeck AB); (7) WarmTouch and CareDrape upper body blanket (Mallinckrodt Medical Inc., St. Luis, MO); and (8) WarmTouch and reusable MultiCover trade mark upper body blanket (Mallinckrodt Medical Inc.) on a previously validated copper manikin of the human body. Heat flux and surface temperature were measured with 11 calibrated heat flux transducers. Blanket temperature was measured using 11 thermocouples. The temperature gradient between the blanket and surface (DeltaT) was varied between -8 and +8 degrees C, and h was determined by linear regression analysis as the slope of DeltaT vs. heat flux. Mean DeltaT was determined for surface temperatures between 36 and 38 degrees C, as similar mean skin surface temperatures have been found in volunteers. The covered area was estimated to be 0.35 m2. RESULTS: Total heat flow from the blanket to the manikin was different for surface temperatures between 36 and 38 degrees C. At a surface temperature of 36 degrees C the heat flows were higher (4-26.6 W) than at surface temperatures of 38 degrees C (2.6-18.1 W). The highest total heat flow was delivered by the WarmTouch trade mark system with the CareDrape trade mark upper body blanket (18.1-26.6 W). The lowest total heat flow was delivered by the Warm-Gard system with the single use upper body blanket (2.6-4 W). The heat exchange coefficient varied between 15.1 and 36.2 W m-2 degrees C-1, and mean DeltaT varied between 0.5 and 3.3 degrees C. CONCLUSION: We found total heat flows of 2.6-26.6 W by forced-air warming systems with upper body blankets. However, the changes in heat balance by forced-air warming systems with upper body blankets are larger, as these systems are not only transferring heat to the body but are also reducing heat losses from the covered area to zero. Converting heat losses of approximately 37.8 W to heat gain, results in a 40.4-64.4 W change in heat balance. The differences between the systems result from different heat exchange coefficients and different mean temperature gradients. However, the combination of a high heat exchange coefficient with a high mean temperature gradient is rare. This fact offers some possibility to improve these systems.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.053
GPT teacher head0.324
Teacher spread0.270 · 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

Citations45
Published2002
Admission routes1
Has abstractyes

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