Bibliographic record
Abstract
Primary mucosal melanomas of the head and neck region are uncommon but aggressive malignancies. These lesions arise from melanocytes located in mucosal membranes, predominantly in the nasal cavity, paranasal sinuses, and oral cavity. Mucosal melanomas account for less than 4% of all melanoma cases and are often missed, due to their occult initial presentations. This is in contrast to cutaneous melanomas, which constitute approximately 85% of melanoma cases and present on surfaces exposed to ultraviolet (UV) radiation. The mainstay of treatment for mucosal melanoma is surgical resection with adjuvant radiotherapy for patients with high-risk features. Despite advancements in surgical techniques, radiotherapy, and even systemic therapies, patients with mucosal melanoma face unfavourable prognoses (5-year disease-free survival <25%) with high rates of locoregional recurrence and/or distant metastases. The present case addresses a 47 year-old man who presents to Otolaryngology with an apparent mucosal melanoma involving the upper lip. This patient was informed of a pigmented lesion on the mucosal surface of the upper left lip three years prior by his dentist. Although largely asymptomatic, the lesion has increased in size. The patient undergoes surgical resection with clear margins and reconstruction. He continues to follow-up to monitor for disease recurrence, having denied adjuvant radiation. This case illustrates the diagnosis and approach to mucosal melanomas and highlights some of the key distinctions between mucosal and cutaneous melanomas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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".