P.118 A rare case of eccrine carcinoma with perineural metastases - a rare cause of facial pain
Bibliographic record
Abstract
Background: Eccrine carcinoma is a rare skin tumor arising in eccrine sweat glands with a predilection for older adults. Over 30% of cases occur in the head and neck. Local and distal metastases are common. Prognosis is poor with regional recurrence in up to 19% of cases. Imaging is indicated in high-risk disease. Methods: We present a case report of eccrine carcinoma in the scalp with perineural metastasis to the left trigeminal nerve. Results: A seventy-nine-year-old male with a history of left temporal scalp pre-cancerous lesion treated with liquid nitrogen two years prior presented with left facial pain and paresthesia. The gadolinium-enhanced MRI head showed a tiny sub-centimetre spiculated subcutaneous lesion in the left temporal scalp and perineural enhancement along the left auricotemporal, V3 and trigeminal nerves. Subsequent excisional biopsy of the temporal lesion showed a poorly differentiated eccrine carcinoma without local perineural invasion. Conclusions: Undifferentiated facial pain is a frequent indication for head imaging, usually with low diagnostic utility. However, scrutiny for perineural enhancement is necessary to avoid missing a potentially deadly process. Eccrine carcinoma is a rare type of skin cancer. Small, painless, indolent primary lesions may be overlooked clinically. Radiologists can affect outcomes in these cases.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".