Comparison of Prognostic Factors for Merkel Cell Carcinoma, Mucosal Melanoma and Cutaneous Malignant Melanoma: Insights into Their Etiologies
Bibliographic record
Abstract
Little is known about the epidemiology of Merkel cell carcinoma (MCC) and mucosal melanoma (MM). Using the United States (US) National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program data, we compared MCC and MM with cutaneous malignant melanoma (CMM) with respect to incidence rates and prognostic factors to better understand disease etiologies. We describe the proportional incidences of the three cancers along with their survival rates based on 20 years of national data. The incidence rates in 2000-2019 were 203.7 per 1,000,000 people for CMM, 5.9 per 1,000,000 people for MCC and 0.1 per 1,000,000 people for MM. The rates of these cancers increased over time, with the rate of MM tripling between 2000-2009 and 2010-2019. The incidences of these cancers increased with age and rates were highest among non-Hispanic Whites. Fewer MCCs and MMS were diagnosed at the local stage compared with CMM. The cases in the 22 SEER registries in California were not proportional to the 2020 population census but instead were higher than expected for CMM and MCC and lower than expected for MM. Conversely, MM rates were higher than expected in Texas and New York. These analyses highlight similarities in the incidence rates of CMM and MCC-and differences between them and MM rates-by state. Understanding more about MCC and MM is important because of their higher potential for late diagnosis and metastasis, which lead to poor survival.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".