Diverse optical characteristic of the prostate and light delivery system: implications for computer modelling of prostatic photodynamic therapy
Bibliographic record
Abstract
OBJECTIVE: To explore the use of photodynamic therapy (PDT) as a minimally invasive form of treatment for organ-confined prostate cancer, for although there are several therapies, ablative treatments are associated with significant morbidity. MATERIALS AND METHODS: Using the photosensitizer tin etiopurpurin, dogs were treated with interstitially placed laser fibres in an effort to validate PDT for treating prostate cancer. Earlier models assumed a uniform distribution of light output from a cylindrical fibre and a uniform attenuation coefficient throughout the prostate. Subsequent observations show that this model was too simple and that light radiance is not linear. To overcome under-treatment, a computer program to complement real-time fibre placement was developed. RESULTS: As light radiance from interstitially placed laser fibres varies significantly from the commonly assumed ideal cylindrical emission, a predictive mathematical model of prostate PDT needs to consider the real emission. Also, the optical properties of the prostate, e.g. absorption and scattering of light, are anisotropic. Differences in the attenuation coefficient (combining absorption and scattering of light) also varied among different animals. Incorporating all these variables into a computer program produced a virtual model of the photo-ablated zone within +/- 2 mm of that observed in animals. CONCLUSION: PDT of the prostate is not trivial and should benefit from computer-aided methods as it is developed for clinical use.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".