Comparative Analysis of Temperature Fields during cryosurgery: Study using the Porosity-based Bioheat Models and Pennes Model
Bibliographic record
Abstract
Cryosurgery plays a crucial role in cancer treatment by utilizing an advanced and minimally invasive surgical technique with significant potential for efficiently eradicating both carcinoma and non-carcinoma tissues through freezing methods.Optimizing this technique is crucial for minimizing both inadequate ablation and collateral damage to healthy tissue during the procedure.Biological tissues, by virtue of their porous nature, offer a network of interconnected spaces between dispersed cells.This intricate network facilitates the efficient delivery of vital nutrients and minerals throughout the tissue.The current study develops a mathematical model integrate bioheat equations that account for varying porosity and apply Pennes equation with variable perfusion, utilizing an effective heat capacity formulation to Capture the intricate details of the phase transitions during freezing and thawing.The transient temperature distribution, propagation of lethal front, and the resulting ablation size within tissue is investigated within the tissue, focusing on understanding the impact of blood vessel sizes, blood velocities, and porosities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".