P129 A prospective comparative audit of outcomes in patients with basal cell carcinoma receiving 10 600-nm carbon dioxide laser-assisted photodynamic therapy with either methyl aminolaevulinate or 5-aminolaevulinic acid as a photosensitizer
Bibliographic record
Abstract
Abstract Photodynamic therapy (PDT) is a well-established nonsurgical management option for low-risk basal cell carcinomas (BCCs). This study compares the efficacy of two photosensitizing agents: methyl aminolaevulinate (MAL) and 5-aminolaevulinic acid (ALA), when used for patients undergoing 10 600-nm carbon dioxide laser-assisted photodynamic therapy. We included all patients with BCC receiving fully ablative laser-assisted PDT at a tertiary centre between November 2021 and November 2024. Lesions were first ablated with a 10 600-nm carbon dioxide laser (SmartXide Touch, DEKA Laser, Calenzano, Italy) under local anaesthetic. The photosensitizer was applied topically according to availability, and incubated for 3 h. The photosensitizer was either MAL 160 mg g−1 cream (Metvix; Galderma, Lausanne, Switzerland) or 5-ALA 78 mg g−1 gel (Ameluz; Biofrontera, Leverkusen, Germany). Photoactivation was by pulsed-dye laser (Cynergy; Cynosure, Westford, MA, USA) or intense pulsed light (Excelight, Lynton, UK). In total 132 lesions were included from 33 patients. Overall, 54.5% were treated with the photosensitizer MAL and 45.5% were treated with ALA. The rolling recurrence rate for all lesions treated with MAL was 2.8%, and with ALA 1.7%. The 2-year recurrence rate for MAL was 5.7% and for ALA 0.0%. Complication rates were low and were 1.4% for MAL and 1.7% for ALA. Cosmetic outcomes were good in both groups, with mean Vancouver Scar Scores of 3.1% and 3.9% for MAL and ALA, respectively. This study concludes that ALA-PDT has a lower overall and 2-year recurrence rate than MAL-PDT. The rate of complications was lower for MAL-PDT than for ALA-PDT, and the cosmetic outcomes for MAL-PDT were slightly better than for ALA-PDT when assessed using mean Vancouver Scar Scores.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".