A Challenging Case of Multiply Recurrent Nasal Basal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: Basal cell carcinoma (BCC) is the most common human malignancy worldwide and represents a significant cost to health care systems. Most cases occur on the head and neck, and many are successfully treated with relatively simple measures. However, if high-risk or complicated cases are not treated effectively, they may result in considerable disfigurement or morbidity. We report on a patient with a complex nasal basal cell carcinoma (BCC) that failed multiple treatments by electrodesiccation and curettage (EDC). Management strategies for primary and recurrent BCC, including EDC, standard excision, Mohs micrographic surgery (MMS), and radiation therapy, are discussed. This case required extensive resection, and we review the literature for predictive factors of significant subclinical spread. OBJECTIVE: To present a complex case that illustrates the management options of high-risk, recurrent BCC of the head and neck. MATERIALS AND METHODS: Case report and review of the literature. RESULTS: MMS offers the lowest recurrence rate in the treatment of recurrent BCC in surgical candidates. A validated risk scale may predict subclinical spread in patients with BCC of the head and neck. CONCLUSIONS: BCC can progress to locally advanced disease, necessitating definitive treatment. EDC performed by an experienced dermatologist may offer cure rates comparable to those of surgery in lower-risk BCC. However, in higher-risk tumors, such as recurrent or larger lesions, methods that ensure clear margins should be considered first line, especially in sensitive locations. The routine use of a validated risk scale can better prepare patients and dermatologists for potentially extensive resections. In cases with risk of extensive involvement, strategies to clearly communicate options and progress at all stages of the process should be available.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".