A new thermal dose model based on Vogel-Tammann-Fulcher behaviour in thermal damage processes
Bibliographic record
Abstract
Thermal dose models are metrics that quantify the thermal effect on tissues based on the temperature and the time of exposure. These models are used to predict and control the outcome of hyperthermia (up to 45°C) treatments, and of thermal coagulation treatments at higher temperatures (>45°C). The validity and accuracy of the commonly used models (CEM43) are questionable when heating above the hyperthermia temperature range occurs, leading to an over-estimation of the accumulation of thermal damage. A new CEM43 dose model based on an Arrhenius-type, Vogel-Tammann-Fulcher, equation using published data, is introduced in this work. The new dose values for the same damage threshold that was produced at different in-vivo skin experiments were in the same order of magnitude, while the current dose values varied by two orders of magnitude. In addition, the dose values obtained using the new model for the same damage threshold in 6 lesions in ex-vivo liver experiments were more consistent than the current model dose values. The contribution of this work is to provide new modeling approaches to inform more robust thermal dosimetry for improved thermal therapy modeling, monitoring, and control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".