012 Photodynamic therapy of non‐melanoma skin cancers with verteporfin and red light – tumor response and cosmetic outcome
Bibliographic record
Abstract
Introduction and objectives: Photodynamic therapy (PDT) with verteporfin may be particularly well suited for patients with multiple low risk non‐melanoma skin cancers (NMSC) while providing good cosmetic outcomes as compared to standard treatments. The objectives of this study were to prospectively assess the tumor responses and cosmetic outcomes of verteporfin‐treated NMSC. Patients and methods: This was a phase 11, open‐label, light‐dose ranging, multicenter study of 54 patients with multiple NMSC. A total of 421 biopsy‐proven tumors that were either basal cell carcinomas or in situ squamous cell carcinomas underwent PDT with intravenous verteporfin 14 mg/m 2 followed by exposure to red light from LED diode arrays (658‐718 nm FWHM) at fluences of 60, 120, or 180 J/cm 2 . Each patient was randomly assigned to receive one of the three light doses to all their tumors. Treated tumors underwent follow up biopsies at 6 months after the initial PDT session to determine the pathologic complete response rate. In addition, the treated tumors were assessed clinically for up to 24 months. The cosmetic outcome of each treated tumor site was assessed by the investigators based on color, profile, and surface texture. Results: The pathologic complete response rates at 6 months were 69, 79, and 93% for the 60, 120 and 180 J/cm 2 light fluences, respectively. The clinical complete response rates by tumor at 6 months were 78, 89 and 98% at 60, 120 and 180 J/cm 2 , respectively, while at 24 months, the corresponding rates were 51, 79, and 95%. The cosmetic outcome by tumor at 24 months judged by the investigator to have achieved at least a satisfactory or higher outcome was 92, 76, and 86% at 60, 120 and 180 J/cm 2 , respectively. Conclusions: PDT of NMSC with verteporfin provides effective tumor clearing that is dose‐dependent with satisfactory to excellent outcomes in the majority of patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".