Efficacy of Imiquimod as an Adjunct to Cryotherapy for Actinic Keratoses
Bibliographic record
Abstract
BACKGROUND: Cryotherapy is the standard of care for clinically apparent (target) actinic keratoses (AKs). Topical imiquimod may reduce initially inapparent or subclinical AKs. OBJECTIVE: We evaluated the potential of topical imiquimod to decrease subclinical AKs after cryotherapy of target AKs. METHODS: A randomized trial of imiquimod or vehicle twice weekly for 8 weeks following 3- to 5-second cryotherapy of target AKs within a 50 cm(2) field at the face or scalp was conducted. Efficacy outcomes included clearance of target, subclinical, and total AKs and proportions clear of AKs. Subjects with residual AKs were offered cryotherapy and open-label imiquimod twice weekly for 8 weeks. RESULTS: Sixty-three subjects completed the randomized phase. At 12 weeks, target AK clearance was similar for imiquimod and vehicle (79% vs 76%), but fewer total AKs were noted for imiquimod (78 vs 116). This was due to a progressive reduction in subclinical AKs with imiquimod compared with a progressive increase with vehicle. More subjects treated with imiquimod achieved clearance of subclinical (58% vs 34%; p = .06) and total (23% vs 9%; p = .21) AKs. CONCLUSION: Imiquimod postcryotherapy may increase clearance of subclinical and total AKs and proportions of subjects clear at 3 months. These findings require confirmation in larger controlled trials powered for statistical significance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".