Regression of Sebaceous Carcinoma of the Eyelid after a Small Incisional Biopsy: Report of Two Cases
Bibliographic record
Abstract
PURPOSE: To report 2 cases of regression of sebaceous carcinoma of the eyelid after a small incisional biopsy. METHODS: Clinical, imaging, and histopathological findings are presented, with a literature review on regressing ocular tumors. RESULTS: Our first patient was a 79-year-old man who presented with a 10-month history of progressive left upper eyelid ptosis caused by an eyelid tumor with orbital involvement and confirmed on magnetic resonance imaging. Our second patient was a 70-year-old woman who presented with ptosis with a left upper eyelid mass. Both patients underwent a small incisional biopsy of their lesion. The histopathological diagnoses in both cases were consistent with sebaceous carcinoma. Both patients refused exenteration. Follow-up clinical examination and imaging disclosed total regression of the ptosis and of the neoplasm with no sign of recurrence in both patients over a 4-year period for Case 1 and a 7-year period for Case 2. CONCLUSION: Regression following incisional biopsy of basal cell, squamous cell, and Merkel cell carcinoma, including of the eyelid, is well documented. To the best of our knowledge, our 2 cases of sebaceous carcinoma are the first to be reported with total involution clinically and on imaging of the tumor following partial incisional biopsy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".