Exploring the rising incidence of neuroendocrine tumors: A population‐based analysis of epidemiology, metastatic presentation, and outcomes
Bibliographic record
Abstract
BACKGROUND: An increased incidence of neuroendocrine tumors (NETs) has been reported worldwide, but the reasons underlying this rise have not been identified. By assessing patterns of metastatic presentation, this study sought to examine the epidemiologic characteristics of NETs and the contribution of early-stage detection to the rising incidence. METHODS: A population-based retrospective cohort study was conducted with prospectively maintained databases linked at the Institute for Clinical Evaluative Sciences. Adult patients with a NET diagnosis from 1994 to 2009 in Ontario, Canada were included. The main outcomes included the overall and site-specific incidence, proportion of metastatic disease, overall survival (OS), and recurrence-free survival (RFS). RESULTS: Five thousand six hundred nineteen NET cases were identified. The incidence of NETs increased from 2.48 to 5.86 per 100,000 per year. Metastases were found in 20.8% at presentation and in another 38% after the initial diagnosis. The proportion of metastases at presentation decreased from 1994 to 2009 (from 29% to 13%). Therefore, although the incidence of all NETs increased, the overall incidence of metastases did not change (0.63-0.69 per 100,000 per year). The 10-year OS rate was 46.5%, and the RFS rate was 64.6%. In addition to the primary tumor site, independent predictors of worse OS included an advanced age (P < .0001), male sex (P < .0001), a low socioeconomic status (P < .0001), and rural living (P = 0.049). CONCLUSIONS: The incidence of NETs has markedly increased over the course of 15 years. This is the first study to provide evidence suggesting that the increase in the incidence of NETs may be due to increased detection. In addition to tumor characteristics, low income and rural residency portend worse survival for patients with NETs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".