The Use of Diphenylcyclopropenone in the Treatment of Recalcitrant Warts
Bibliographic record
Abstract
BACKGROUND: The treatment of recalcitrant palmoplantar and periungual warts using topical immunotherapy with diphenylcyclopropenone (DPC) was reviewed retrospectively over a seven-year period. METHODS: Two hundred eleven patients were sensitized during this time. The patients consisted of 90 males and 121 females and were between 5 and 78 years old. Twenty-three patients were lost to followup. Of the remaining, 4 were undergoing treatment at the time of evaluation, 1 patient failed sensitization, and 1 patient became pregnant. Four discontinued because of side effects, 3 because of financial reasons, and 18 patients discontinued treatment prior to completing the minimum required applications (defined as 6), producing a dropout rate of 12% (25/211). Three patients had additional treatment during the course of DPC and were not included in the study. The remaining 154 patients were classified as nonresponders or responders. RESULTS: The responders consisted of 135 individuals (87.7%) that had complete clearance of warts. Reported adverse effects were local and included with pruritus (15.6%), with blistering (7.1%), and with eczematous reactions (14.2%). The majority of the patients tolerated the treatment very well. One patient developed local impetigo. Patients had an average of 5 treatments over a 6-month period. CONCLUSIONS: Topical immunotherapy using DPC is an effective treatment option for recalcitrant warts. It should be considered as first-line treatment for warts based on its high response rate, absence of scarring, and painless application.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".