Epidermal Carcinoma of the Conchal Bowl: Creation of a Multidisciplinary Pathway Approach
Bibliographic record
Abstract
BACKGROUND: Malignant neoplasms of the auricle make up 6% of all skin cancers. Management of cutaneous neoplasms of the conchal bowl presents a unique challenge in visualizing and defining margins that may extend into the external auditory canal (EAC). OBJECTIVES: The objective of this study was to create a multidisciplinary pathway for cutaneous carcinoma of the conchal bowl extending into the EAC. METHODS: We present a series of patients that were referred to dermatology or otolaryngology, with cutaneous neoplasms arising in the conchal bowl. A consensus approach from otolaryngology and dermatology, for evaluation and treatment, was created based on evaluation of these cases, and review of the otolaryngology and dermatology literature, in collaboration between the two specialties. RESULTS: Initial evaluation should be done by both dermatology and otolaryngology, with otomicroscopic evaluation of the canal. Imaging is recommended for lesions that approach the EAC, for bony and soft tissue spread. Excision of the tumor with Mohs micrographic surgery to achieve clearance in the conchal bowl should be performed initially. If extension into the external auditory meatus is present, otolaryngology would proceed with en bloc resection. Repair is dictated by the defect, with both specialties involved in follow-up. CONCLUSIONS: In collaboration between dermatology and otolaryngology, and following review of the literature, a pathway was created to manage skin cancer of the conchal bowl. This resulted in a stepwise approach for evaluation and management, ensuring that patients have a streamlined pathway for the treatment of these lesions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".