A comparison of two probes to determine rectum optical properties
Bibliographic record
Abstract
Tissue optical properties are crucial for determining the light dose delivered to the tumor. Two probes are compared: the two-catheter probe is based on transmittance measurement between one point source and one isotropic detector inside parallel catheters spaced at 0.5 cm along a 1-inch diameter transparent cylinder; and a 1-inch trans-rectal diffuse optical tomography (DOT) probe designed for prostate measurements, using a multiple fiber-array with source-detector separations of 1.4-10 mm. The two-catheter probe uses an empirical model for primary and scatter light fluence rates in the cylindrical cavity condition for anal PDT to determine optical properties along the source catheter using dual motors to move the source and detector along the catheters. The DOT probe uses finite element method (FEM) to obtain distribution of optical properties in 3D. Validations for the two probes were performed in liquid and solid phantoms. For each method, validation was performed in tissue-mimicking liquid phantoms for a range of known optical properties (μa between 0.05 and 0.9 cm-1 and μs’ between 5.5 and 16.5 cm-1). To cross-check the two methods, solid phantoms were created of known optical properties at the University of Pennsylvania and sent for measurement to Princess Margaret Cancer Centre (PMH) to mimic realistic patient simulating conditions. Measurements were taken and optical properties were then recovered without knowing the expected values to cross-validate each probe. The results show modest agreement between the measured μa and μs’values, but high degree of agreement between the measured μeff performed independently using the two methods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".