Topical hydrogen peroxide as a neoadjuvant treatment in the surgical excision of non-melanoma skin cancers
Bibliographic record
Abstract
Introduction: Hydrogen peroxide is used as a topical antiseptic and hemostatic agent. At higher concentrations, it can induce cell death and has recently been reported to be effective in treating seborrheic keratosis. This study examines the effectiveness of topical hydrogen peroxide to shrink non-melanoma skin cancers of the head and neck prior to excision with the goals of minimizing cost and morbidity. Methods: The protocol involves rubbing a solution of 33% hydrogen peroxide into the lesion and a 1 cm border with a cotton tip applicator until blanching is observed. The process can be repeated after one hour and weekly reapplications to a maximum of three times are included in this study group. At one month from the initial application, the remaining lesion is resected with primary closure. The specimen is sent to pathology for histological analysis and final diagnosis. The study will accrue 50 patients with one or more lesions per patient. Measurements of lesion size are recorded by tracing the border on to clear acetate film at each visit. Results: Initial results from first six patients found a range of responses from no size change to no visible lesion remaining for excision. All excised specimens have had negative margins histologically. Summary: Topical hydrogen peroxide is a simple and effective treatment for reducing the size of non-melanoma skin cancers prior to excision.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".