<title>Predicting fluence measurements from a cylindrical diffusing tip using the P3-approximation</title>
Bibliographic record
Abstract
Photodynamic Therapy (PDT) is becoming a popular treatment modality for many superficial cancers and several non-malignant diseases. The use of PDT to treat solid tumors has been limited because there is currently is no clinically acceptable method of either characterizing the optical parameters of a target tissue or of determining dose delivered to the target tissue volume as delivered by a fibreoptic terminated in a cylindrical diffusing tip. Accurate light dosimetry methods, such as the Monte Carlo method, are of limited clinical utility. In this paper we describe the use of the P3-Approximation to optically characterize a light scattering and absorbing medium. Tissue characterization calculations made using the P3-Approximation are analytical and may be performed much faster than with numerical modeling. The process time needed to estimate the dose distribution is sufficiently short to permit iterative adjustment of the light source distribution to account for tissue inhomogeneities, in real time. P3-Approximation based optical dosimetry should be sufficiently fast to be incorporated into an iterative feedback control algorithm for the distribution of light across an interstitial source array. With the optical coefficients furnished by the P3-Approximation, the light dose delivered to the medium from a cylindrical fibreoptic source can be accurately calculated using a novel formulation of Diffusion Theory. The cylindrical fibreoptic diffuser is modeled as a Huygen's array: a finite yet continuous array of isotropic point sources. The predicted light fluence distribution from the Huygen's array compares favorably with experimental fluence measurements.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".