Second-intention healing combined with aminolaevulinic acid photodynamic therapy in the treatment of periocular basal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Basal cell carcinoma (BCC) is the most common skin cancer. Recently, aminolaevulinic acid photodynamic therapy (ALA-PDT) has emerged as a noninvasive treatment for facial lesions. OBJECTIVES: To evaluate the efficacy and safety of second-intention healing after tumour excision with ALA-PDT in periocular BCC. METHODS: In total, 16 patients with periocular BCC from an academic, large, metropolitan-based hospital were included in this study. All the patients received marginal resection combined with topical ALA-PDT. No suture was performed, and ALA-PDT treatments (three to six times) were applied to each patient with 633-nm red light directly after surgical resection. Patient demographics, images, medication and prognosis were evaluated and recorded. Patient Satisfaction Questionnaire (PSQ) scores were recorded, consisting of overall satisfaction with the medical experience (scored 0-10) and cosmetic outcome (scored 0-10). RESULTS: Ten women and six men aged 33-84 years with periocular BCC were included. Subtypes of periocular BCC were nodular (8 of 16), infiltrative (2 of 16), nodular cystic (2 of 16), ulcerative (1 of 16) and unknown (3 of 16). All patients completed the treatment and had good overall satisfaction with the medical experience (mean score 9.5) and good cosmetic outcome (mean score 8.6). No serious complications or scar formation were reported in any patient. No recurrence of tumour was observed in the long-term follow-up of 1-3.5 years. CONCLUSIONS: Our results indicate that second-intention healing after marginal resection combined with ALA-PDT can reduce the tissue defect and maintain excellent cure rates. This method offers an alternative and aesthetic treatment for localized periocular BCC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".