Treatment Strategies and Survival Trends for Anorectal Melanoma: Is it Time for a Change?
Bibliographic record
Abstract
BACKGROUND: Immunotherapy advances for the treatment of cutaneous melanoma question its efficacy in treating anorectal mucosal melanoma (ARMM). We aimed to identify the prevalence, current management, and overall survival (OS) for ARMM. METHODS: Review of patients with ARMM from 2004 to 2015 National Cancer Database. Factors associated with immunotherapy were identified using multivariable logistic regression. The primary outcome was 2- and 5-year OS. Subgroup analysis by treatment type was performed. RESULTS: A total of 1331 patients were identified with a significant increase in prevalence (2004: 6.99%, 2015: 10.53%). ARMM patients were older, white, on Medicare, and from the South. The most common treatment was surgery (48.77%), followed by surgery + radiation (11.75%), surgery + immunotherapy (8.68%), and surgery + chemotherapy (8.68%). 16.93% of patients received immunotherapy, with utilization increasing (7.24%: 2004, 21.27%: 2015, p < 0.001). Patients who received immunotherapy had a significantly better 2-year OS (42.47% vs. 49.21%, p < 0.001), and other therapies did not reveal a significant difference. Adjusted analysis showed no difference in 2- and 5-year OS based on therapy type. CONCLUSION: The prevalence of ARMM has increased. The use of immunotherapy has increased substantially. Some survival benefit with the administration of immunotherapy may exist that has yet to be revealed. A more aggressive treatment paradigm is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".