Marjolins Ulcer: Clinicopathological Profile and Treatment Patterns
Bibliographic record
Abstract
Purpose: Marjolins ulcer is a malignant transformation arising from chronic ulcers or previously traumatized scars that occur usually after burns. This article aims to study the clinicopathological profile and treatment patterns of Marjolins ulcer. Materials and Methods: Retrospective analysis of all Marjolins ulcer patients presented to Vydehi Cancer Centre from 2018 to 2021 was done. A total of 27 patients of all age groups were included in the study. All information regarding detailed history, clinical examination, treatment details were retrospectively collected and analysed. Results: Most of the patients were in the 5th decade of life with an overall male preponderance. The most common cause for Marjolins ulcer was Burns scars followed by Trauma. Lower extremities were found to be the most predominant site. The mean latency period for the development of Marjolins ulcer was 11 years. Squamous cell carcinoma was the most common histological subtype, Adjuvant Radiotherapy was given to the patients with high-risk features. Conclusion: Chronic non-healing ulcers that do not respond to treatment should be carefully examined for malignant transformation. Surgery is the mainstay of treatment and Adjuvant Radiotherapy should be considered in high-risk cases to reduce locoregional recurrence.
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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".