Photodynamic Therapy Healing a Refractory Radiation-Induced Ulcer on the Chest Wall Postmastectomy Radiotherapy for Breast Cancer: A Case Report and Literature Overview
Bibliographic record
Abstract
Photodynamic therapy (PDT) has been used to treat cancers. It has also been used to treat infectious diseases and inflammatory conditions. PDT promotes wound healing, while clinical use of PDT for wound healing is uncommon and not thoroughly investigated. We report a 75-year-old female with a radiation-induced non-healing ulcer for five years on the chest wall postmastectomy radiotherapy. Biopsy showed epidermal erosion with dermal inflammation but no recurrent cancer. She was referred from the wound care clinic after multiple unsuccessful attempts to manage wound healing for two years involving daily home nursing visits. PDT was discussed with the patient who consented to PDT instead of hyperbaric oxygen therapy (HBOT) for fear of its side effects. Her wound improved after a total of three treatments and the process of wound healing continued for 14 months since her first treatment session. The presented case supports the beneficial effects of PDT on chronic ulceration impeding healing of a postmastectomy radiotherapy wound. To our knowledge, this report is unique in documenting details of PDT healing a chronic refractory ulcer of five years, which developed after cancer therapy (mastectomy and radiotherapy). Further clinical study of PDT is needed on wound healing post-surgery and radiation in cancer patients. An overview of HBOT in comparison with PDT for wound healing is presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".