Effectiveness of photodynamic therapy for mammary and extra-mammary Paget's disease: a state of the science review
Bibliographic record
Abstract
BACKGROUND: Paget's disease is a rare skin disorder occurring in the breast (mammary) or in the groin, genital, peri-anal and axillary regions (extra-mammary). Typical treatment involves surgical excision, which in the case of extra-mammary Paget's disease, can lead to significant morbidity. Photodynamic therapy (PDT) which uses a topical or intravenous photosensitizing agent that is activated by a light source to ablate abnormal tissue, offers a minimally invasive alternative. The purpose of this study was to assess the effectiveness of photodynamic therapy in the treatment of Paget's disease. METHODS: Following Cochrane guidelines, a comprehensive systematic review of all clinical studies and reports examining the use of PDT for mammary and extra-mammary Paget's disease was conducted. Study quality was assessed using the Oxford Levels of Evidence Scale. RESULTS: 21 retrospective and 2 prospective non-comparative studies were identified and included in the review: 9 case reports with 1-2 patients and 14 case series with 1-16 patients. These reports totalled 99 patients with 133 extra-mammary Paget's lesions and 3 patients (with 3 lesions) with mammary Paget's disease. Follow-up periods were typically one year or less, with 77/133 extra-mammary lesions exhibiting complete response to PDT. One recurrent mammary skin lesion and two mammary lesions treated concomitantly with surgery also exhibited complete responses. CONCLUSIONS: Evidence of the effectiveness of PDT for Paget's disease is promising, but limited. This may, in part, be explained by the rarity of the condition, making controlled comparative clinical trials challenging.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".