Photodynamic Diagnosis and Therapy for Peritoneal Carcinomatosis from Gastrointestinal Cancers: Status, Opportunities, and Challenges
Bibliographic record
Abstract
Selective accumulation of a photosensitizer and the subsequent response in only the light-irradiated target are advantages of photodynamic diagnosis and therapy. The limited depth of the therapeutic effect is a positive characteristic when treating surface malignancies, such as peritoneal carcinomatosis. For photodynamic diagnosis (PDD), adjunctive use of aminolevulinic acid- protoporphyrin IX-guided fluorescence imaging detects cancer nodules, which would have been missed during assessment using white light visualization only. Furthermore, since few side effects have been reported, this has the potential to become a vital component of diagnostic laparoscopy. A variety of photosensitizers have been examined for photodynamic therapy (PDT), and treatment protocols are heterogeneous in terms of photosensitizer type and dose, photosensitizer-light time interval, and light source wavelength, dose, and dose rate. Although several studies have suggested that PDT has favorable effects in peritoneal carcinomatosis, clinical trials in more homogenous patient groups are required to identify the true benefits. In addition, major complications, such as bowel perforation and capillary leak syndrome, need to be reduced. In the long term, PDD and PDT are likely to be successful therapeutic options for patients with peritoneal carcinomatosis, with several options to optimize the photosensitizer and light delivery parameters to improve safety and efficacy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".