Red light photodynamic therapy with 10% aminolevulinic acid gel showed efficacy for treatment of superficial basal cell carcinoma in a randomized, vehicle controlled, double-blind, multicenter phase III study
Bibliographic record
Abstract
BACKGROUND: Surgical procedures remain the gold standard for treating basal cell carcinoma (BCC), although commonly associated with cosmetic defects. The demand for noninvasive alternatives remains high. OBJECTIVE: To determine efficacy and safety of red light photodynamic therapy (PDT) with 10% 5-aminolevulinic acid (ALA) gel vs vehicle for treatment of superficial BCC. METHODS: This randomized, double-blind, vehicle-controlled, pivotal phase III study was conducted at 21 centers in the US. Eligible participants had ≥1 naïve superficial BCC and received 1-2 PDT cycles (2 PDTs each cycle) followed by clinical and histological assessment 12 weeks after start of the last PDT cycle. RESULTS: Of 187 randomized participants, 145 received PDT with 10% ALA gel and 42 with vehicle. Histological clearance was 75.9% with 10% ALA gel vs 19.0% with vehicle (P < .0001). Clinical clearance was 83.4% with 10% ALA gel vs 21.4% with vehicle (P < .0001). A total of 88.1% of participants treated with 10% ALA gel rated the esthetic outcome as very good or good. No previously unknown adverse events occurred. LIMITATIONS: Few participants with lesions on face/scalp; to date, a 60-month follow-up is still ongoing. CONCLUSION: 10% ALA gel showed significantly higher clearance rates than vehicle with unproblematic safety and positive esthetic 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".