Uncertainty and Sensitivity Analysis for a Tissue Laser-Irradiation Tissue Model
Bibliographic record
Abstract
The modeling and control of laser-irradiated tissue is a challenging problem due to the non-linear behavior of the tissue when heated. This is compounded by the fact that in-vivo tissue parameters are not well known. In order to properly model and design control methods for LITT, it is necessary to quantify the uncertainties in the model parameters and their effect on the variability of the final output. For this purpose, a non-linear LITT model has been developed and an uncertainty and sensitivity analysis of the model was performed. This was also done to identify the parameters which have the largest contribution to the uncertainty in the output. The uncertainty analysis revealed that uncertainties in the model parameters can result in a variance of up to 42degC (40% of the mean) in the predicted temperature. The sensitivity analysis showed that thermal parameters have a larger effect on the predicted temperature and thermal dose than optical parameters. The analysis also showed that changes in the specific heat and mass density had the largest effect on the model output early in a treatment, while thermal conductivity had the largest effect later in the treatment. These results are being used to develop a framework for controlling LITT
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".