Lesional therapies for in‐transit melanoma
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To describe the outcomes of lesional therapy of in-transit melanoma (ITM) with interleukin-2 (IL-2), diphencyprone (DPCP), combination lesional therapy (IL-2, retinoid, and imiquimod; CLT), and imiquimod. METHODS: Data was collected for consecutive patients with ITM receiving lesional therapies from 2008 to 2018 in a retrospective review. Included patients did not have metastatic disease at time of starting on lesional therapy and were not on systemic therapy. The primary outcome was complete pathologic response (pCR). RESULTS: Of 83 patients, 57 (69%) started treatment with IL-2, 10 (12%) with DPCP, 12 (14%) with CLT, and 4 (5%) with imiquimod. pCR was achieved in 34 patients (41%) overall, including 44% starting on IL-2, 20% on DPCP, 58% on CLT, and none on imiquimod (P = .024). With a median follow-up of 45 months, cumulative one-year overall survival was 86%, with the best survival in the CLT group. Forty-eight percent experienced common terminology criteria for adverse events grade 1 or 2 toxicity. A quarter of patients on DPCP discontinued therapy due to toxicity (P = .002). CONCLUSIONS: IL-2 may be considered for the treatment of ITM with multiple or rapidly developing lesions where there would otherwise be significant morbidity with surgery, given pCR rates and toxicity.
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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 | 0.000 | 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".