33% hydrogen peroxide as a Neoadjuvant treatment in the surgical excision of non-melanoma skin cancers: A case series
Bibliographic record
Abstract
Background Hydrogen peroxide (H2O2) is a product of respiration in mitochondria and an important oxidizing agent in biological systems. Previous investigations have shown the efficacy of H2O2 in treating skin conditions such as seborrheic keratosis and actinic keratosis. In an area like the face, reconstruction of excision defects and ultimately aesthetic outcomes are of utmost importance. Hydrogen peroxide may represent a simple yet effective method at shrinking non-melanoma skin cancers (NMSC) of the head and neck before they are excised. Methods Eleven consecutive patients presenting to our cutaneous malignancy clinic had their skin lesions evaluated by the senior author for participation in the study. Lesion length and width was measured. Hydrogen peroxide formulated at a concentration of 33% was rubbed into the lesion until blanching was observed. Lesions were re-measured at follow up. Excisional biopsy was then performed and histopathological diagnosis was obtained. Statistical analyses compared pre- and post-treatment lesion dimensions. Results Seventeen biopsy-proven NMSC lesions were included in this investigation. Statistically significant reductions in the length ( p < 0.001) and width ( p < 0.001) were observed with H2O2 treatment. For some lesions, H2O2 was the sole treatment required, with post-treatment biopsy demonstrating no evidence of malignancy. Patients endured minimal discomfort during treatment and no long-term side effects were observed. Follow up at 6 months revealed no recurrences. Conclusions We have demonstrated a significant reduction in the size of multiple lesions after application of 33% hydrogen peroxide, simplifying definitive excision and reconstruction. Hydrogen peroxide demonstrated an ability to successfully treat non-melanoma skin cancers as well.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".