Merkel Cell Carcinoma of the Skin
Bibliographic record
Abstract
BACKGROUND: Neuroendocrine/Merkel cell carcinoma (MCC) of the skin is an uncommon tumour. Currently, there are only limited data available on the natural history, prognostic factors, and patient management of MCC. OBJECTIVES: To review our experience and build the largest database from the literature. METHODS: Twenty-eight cases from the London Regional Cancer Center were combined with 633 cases obtained from the literature searched in English, French, German, and Chinese for the years 1966 to 1998. The database included age, sex, initial disease status at presentation to the clinic, site of primary, any coexisting disease, any previous irradiation, sizes of primary/nodal/distant metastases, management details, and final disease status. A new modified staging system was used: stage Ia (primary disease only, size > 2 cm), stage Ib (primary disease only, size > 2 cm); stage II (regional nodal disease), and stage III (beyond regional nodes and/or distant disease). RESULTS: Age > 65 years, male sex, size of primary > 2 cm, truncal site, nodal/distant disease at presentation, and duration of disease before presentation (< or =3 months) were poor prognostic factors. Surgery was the initial treatment of choice and it significantly improved overall survival (p =.004). CONCLUSIONS: We identified poor prognostic factors that may necessitate more aggressive treatment. The suggested staging system, incorporating primary tumour size, accurately predicted outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".