Treatment planning evolution: Comparing approaches in photodynamic and radiation therapies
Bibliographic record
Abstract
The setups of previous and ongoing clinical trials are based on prescribed PDT doses, PS concentration, and light intensity derived from averages of previous clinical or study populations. It is understood that monitoring of personalized PS and light dose is needed to improve PDT outcomes. Monitoring of photophysical, photochemical, or cytotoxic moieties is common, representing concepts of delivered, absorbed, or equivalent doses similar to those used in radiation therapy (RT). Unlike RT, these dose concepts are not equally well developed and standardized across the PDT clinical indications; however, there is potential to improve PDT treatment setup, planning, and delivery by leveraging methodologies from RT. This review summarizes dose definitions and advancements in RT treatment planning and presents the equivalent dose concepts for PDT, particularly how these concepts can expand on the existing methods for PDT treatment planning. By identifying the major limitations and areas for improvement in PDT planning, the hope is to stimulate preclinical and clinical research studies that can enhance the efficacy of PDT, improving patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".