A 36-month clinical experience of the effectiveness of curettage and imiquimod 5% cream in the treatment of basal cell carcinoma.
Bibliographic record
Abstract
BACKGROUND: Electrodesiccation and curettage is commonly used for the treatment of basal cell carcinomas (BCCs). Does the addition of imiquimod 5% cream improve clearance rates and cosmetic outcomes? OBJECTIVE: To evaluate a 3-year clinical experience of the effectiveness of curettage combined with imiquimod cream in the treatment of BCC. METHODS: Patients were enrolled into the study in the first 10 months of 2003. All patients had biopsy-confirmed BCCs and were treated with curettage followed by imiquimod 5% cream 5 times weekly for 6 weeks. RESULTS: Ninety patients with 101 tumors were treated; a clearance rate of 96% was obtained. Twenty-five sites were rebiopsied at 6 weeks after therapy, regardless of clinical findings. Two of these biopsies showed persistent BCC. The remaining 76 sites were followed clinically and only rebiopsied for clinical signs of reoccurrence. Two additional BCCs reoccurred at 23 months and 25 months, respectively. All patients were followed a minimum of 13 month with an average of 36 months. There were minimal cutaneous side effects and no systemic side effects. CONCLUSIONS: Curettage followed by the application of imiquimod 5% cream resulted in clearance rates of 96% at an average 36 months follow-up. The treatment was well-tolerated and appears to produce a favorable cosmetic outcome.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".