AAPM Task Group Report 274: Fluence rate dosimetry for photodynamic therapy (PDT)
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a treatment modality clinically approved for several oncologic indications, including esophageal and endobronchial cancers, precancerous conditions including Barrett's esophagus and actinic keratosis, and benign conditions like age-related macular degeneration. While it is currently clinically underused, PDT is an area of significant research interest. Because PDT relies on the absorption of light energy by intrinsic or administered absorbers, the dosimetric quantity of interest is the absorbed energy per unit mass of tissue, proportional to the fluence rate of light in tissue. It has been demonstrated that the fluence rate at the tissue surface may differ significantly from the incident irradiance of light because of multiple scattering and absorption, both of which may vary among patients and tissue types. This report will review the current state-of-the-art fluence rate dosimetry technology. It will describe the two types of detectors currently available for fluence rate measurement, scattering-tip and fluorescence-based detectors, and review their principles of operation. The report will recommend strategies to establish calibration and quality assurance procedures for clinical fluence rate dosimetry equipment, and it will establish guidelines for clinical implementation of fluence rate dosimetry.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".