Laser thermal therapy: utility of interstitial fluence monitoring for locating optical sensors
Bibliographic record
Abstract
Multipoint optical fluence measurements can potentially be used to detect coagulation-induced changes in optical propagation during interstitial laser thermal therapy. Estimating the dimensions of coagulation using on-line optical monitoring, which is applicable to treatments where the tip of the source fibre is not precharred, may be limited by the accuracy of the placement of optical sensors with respect to source fibres. A strategy has been developed to determine accurately the position of a four-sensor linear array, prior to treatment, using optical fluence data obtained from the sensors for low-power (< or = 0.5 W) irradiation. A minimum of four sensors in an array was required in order to develop a mathematical formulation for position determination that did not require tissue optical properties or laser power as input. Optical propagation was based on diffusion theory for homogeneous tissues in spherical geometry. Low input laser power is needed to ensure that there are no thermally induced changes in tissue optical properties not accounted for in the mathematical description. Experimental evaluation was performed in a tissue-equivalent liquid phantom using 0.5 W of 805 nm optical energy and a translatable isotropic optical sensor. For sensor locations with 2 mm spacing, placement accuracy of 0.67 mm was achieved. The accuracy improved to 0.13 mm as the sensor spacing increased to 5 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".