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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".