A Real-World, Population-Based Study for the Incidence and Outcomes of Neuroendocrine Neoplasms of Unknown Primary
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to provide a real-world, population-based assessment of the incidence and outcomes of neuroendocrine neoplasms (NENs) of unknown primary. METHODS: Surveillance, Epidemiology, and End Results registry was accessed, and cases with NENs of unknown primary were reviewed. Rates of NENs diagnosis 1975-2017 according to primary tumor site were also reviewed. Survival outcomes of patients with NENs of unknown primary compared to metastatic NENs with known primary were determined through Kaplan-Meier estimates and multivariable Cox regression analysis. Overall and cancer-specific survival analyses were stratified by primary site and histology (neuroendocrine tumor vs. neuroendocrine carcinoma). RESULTS: A total of 3,550 cases (7%) were diagnosed with NENs of unknown primary within the study duration. The annual percent change for NENs of unknown primary was 3.4 (95% CI: 2.6-4.2). Within the cohort of metastatic neuroendocrine tumor patients (carcinoid tumor histology), the following factors were associated with a lower risk of death; younger age (HR: 0.477; 95% CI: 0.443-0.513), female sex (HR: 0.922; 95% CI: 0.860-0.989), and small intestinal primary (HR for unknown primary vs. small intestinal primary: 1.532; 95% CI: 1.408-1.668). Within the cohort of metastatic neuroendocrine carcinoma, the following factors were associated with a lower risk of death in this cohort; younger age (HR: 0.646; 95% CI: 0.612-0.681), female sex (HR: 0.843; 95% CI: 0.801-0.888), and small intestinal primary (HR for unknown primary vs. small intestinal primary: 2.961; 95% CI: 2.586-3.391). CONCLUSIONS: The diagnosis of NENs of unknown primary has increased across the past 4 decades. Outcomes of individuals with metastatic small intestinal NENs seem to be better than those with metastatic NENs of unknown primary (for both carcinoid tumors and neuroendocrine carcinomas).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".