Cutaneous Horn: A Devil Not Only in Appearance
Bibliographic record
Abstract
Background: The pathology of most cutaneous horns are benign although malignancy potential has been reported in around 20 to 30 percent and not only the appearance of these lesions are a source of distress but also the fact that they can be associated with malignant transformations. The purpose of this article is to keep the clinician in constant vigil of this uncommon benign but potentially malignant lesions. Objective: To evaluate the association of Cutaneous horn with malignant change Methods: The six patients with cutaeous horn presented in our outpatient department of Plastic and Reconstructive surgery from 1st January 2014 to 30th November 2015 were evaluated and managed with wide local excision and reconstruction considering the principals of the reconstructive ladder. Histopathological reports of the specimen were collected and data was tabulated. The followup clinical examination was done every week. Results: In our study most of the lesions occured on the scalp and the remaining were found on the perineum and malignancy was confirmed on histopathology in half of these scalp lesions, whereas none of the perineal lesions in our sudy showed any malignant change. Conclusions: Cutaneous horn although an uncommon condition and usually neglected by the patient should be kept under high suspicion by the treating Surgeon and definitive management with wide local excision with adequate margins should always be explained to the patient.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".