Malignant Skin Cancer Excision in Combined Therapy with Electro-Chemotherapy and Dermal Substitute
Bibliographic record
Abstract
Squamous cell carcinoma (SCC) is the second most common malignancy skin cancer. It is characterized by abnormal, accelerated growth of squamous cells (SCs). SCC occurs when DNA damage from exposure to ultraviolet radiation or other damaging agents trigger abnormal changes in the SCs, presenting as painless lesions on areas of high sun exposure, such as the dorsum of the hand and upper extremity. For most skin SCC, the surgical excision alone is standard practice. However, recent efforts in new treatment strategies have involved around adjuvant or concomitant electrochemotherapy (ECT). ECT is a non-thermal tumor ablation modality, safe and effective on any type of solid tumor. An 87-year-old patient affected by hand SCC with invasion of deep structures including tendons was treated with neoadjuvant intra-tumoral ECT sessions followed by a selective surgical removal and reconstruction of the substance loss with collagen dermal template (CDT). Two neoadjuvant intra-tumoral ECT procedures, at distance of 3 months, with the aim to reduce the tumor size before a selective surgery, were performed. This study shows that combined surgical selective excision with ECT and CDT is a valid technique for the extended-deep dorsal hand tumor lesions reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".