Efficacy of intralesional bleomycin in treatment resistant viralwarts
Bibliographic record
Abstract
Introduction: Optimal management of treatment-refractory viral warts caused by human papillomavirus is unknown.One of the treatment methods is intralesional bleomycin solution.Objective: To determine risk factors for resistant viral warts (not responding to conventional treatments for ≥ 6 months), to determine the effectiveness and safety of intralesional bleomycin in a group of patients with viral warts resistant to conventional treatment methods, and to assess the utility of dermoscopy in monitoring treatment effects during intralesional bleomycin therapy. Material and methods:The study group consisted of consecutive 12 adult patients with resistant viral warts treated with intralesional bleomycin (0,5 U/ml) at the Department of Dermatology, Venereology and Allergology, Medical University of Gdansk between July 2019 and December 2021.Inclusion criteria were age > 18 and previous unsuccessful treatment of viral warts with ≥ 2 methods used according to guidelines over a period of 6 months.The control group consisted of 8 adult patients who presented with viral warts of the total duration of less than 6 months with no previous treatment, and qualified for cryotherapy.Results: Bleomycin showed 100% efficacy.Except for periprocedural pain, no side effects were observed.Dermoscopy proved to be effective in clinical evaluation of patients, as it allowed to differentiate wart remnants from eschar observed after bleomycin injection.In one patient we observed CD4+ lymphocytopenia at the inclusion stage, and no other risk factors of resistant warts could be identified, however a relatively small number of patients studied could influence this observation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".