A Review of Methods for Quantifying Tissue Thermal Conductivity
Bibliographic record
Abstract
Abstract Analytical and numerical modelling of heat transfer in soft tissues can help improve the safety and efficacy of thermal therapies and aid in characterizing the extent of tissue thermal effects such as thermal damage. Modelling of tissue temperatures requires several thermophysical properties as well as description of the thermal contribution of blood flow or perfusion, as appropriate. One of the essential properties is the thermal conductivity of the tissue for which no suitable standard measurement method exists. Thermal conductivity varies with the type of tissue and its composition in terms of water content, fat, and protein. Because these constituents vary from sample to sample and with time, measuring tissue thermal conductivity poses significant challenges and standardized methods have yet to emerge. This review critically examines common and emerging methods that have been used to determine thermal conductivity of tissue. Although some of the techniques (e.g., the guarded hotplate) are standardized for use with inorganic materials, extending their application to measuring tissues is not straightforward. Some of the methods are applicable only to ex vivo tissue samples, which may be advantageous as they avoid uncertainties associated with blood perfusion. Methods applicable to in vivo tissues require description of the contribution of the blood perfusion to estimate the thermal conductivity accurately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".