A semi-empirical treatment planning model for optimization of multiprobe cryosurgery
Bibliographic record
Abstract
A model is presented for treatment planning of multiprobe cryosurgery. In this model a thermal simulation algorithm is used to generate temperature distribution from cryoprobes, visualize isotherms in the anatomical region of interest (ROI) and provide tools to assist estimation of the amount of freezing damage to the target and surrounding normal structures. Calculations may be performed for any given freezing time for the selected set of operation parameters. The thermal simulation is based on solving the transient heat conduction equation using finite element methods for a multiprobe geometry. As an example, a semi-empirical optimization of 2D placement of six cryoprobes and their thermal protocol for the first freeze cycle is presented. The effectiveness of the optimized treatment protocol was estimated by generating temperature-volume histograms and calculating the objective function for the anatomy of interest. Two phantom experiments were performed to verify isotherm locations predicted by calculations. A comparison of the predicted 0 degrees C isotherm with the actual iceball boundary imaged by x-ray CT demonstrated a spatial agreement within +/-2 mm.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".