<title>Evaluation of a fuzzy logic controller for laser thermal therapy</title>
Bibliographic record
Abstract
Laser thermal therapy (LTT) is a minimally invasive surgical technique used to destroy solid tumors while minimizing damage to adjacent normal tissues. Optical energy, delivered through fibers implanted into the target volume, raises tissue temperatures above 60 degree(s)C resulting in coagulative necrosis (thermal damage). Thermal damage volumes, however, can be irregular and unpredictable, resulting from dynamic changes in the tissue properties during treatment. A closed-loop feedback fuzzy logic controller for LTT was developed with the tissue treated as a black-box system. Preliminary testing was conducted for simulated LTT with a single spherically emitting source fiber at the center of 5 mm and 10 mm diameter target tissues. Dynamic changes in blood perfusion and tissue optical properties due to heating were incorporated into the LTT simulator. Input laser power was modulated to control the temperature field in an attempt to reach target temperatures at the source (90 degree(s)C to avoid tissue charring) and at the target boundary (55 degree(s)C). In all simulations, thermal damage based on Arrhenius formulation ((Omega) equals 1) was reached at the target boundary. The controller also responded efficiently to unexpected, rapid temperature changes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".