A Real-World Study of the Incidence and Outcomes of Early-Onset Well-differentiated Neuroendocrine Neoplasms
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the incidence and outcomes of adults with early-onset (20 to 34 y) diagnosis of well-differentiated neuroendocrine neoplasms. METHODS: Surveillance, Epidemiology, and End Results (SEER)-18 database was accessed, and patients with well-differentiated lung or digestive tract neuroendocrine neoplasms diagnosed 2000 to 2018 were reviewed. Annual percent changes (APCs) were calculated for the 3 disease subsites (foregut, midgut, and hindgut) stratified by age group. Kaplan-Meier survival estimates/log-rank testing were used to examine differences in overall survival between the 3 age groups. Multivariable Cox regression analyses were used to evaluate factors affecting overall and cancer-specific survivals. RESULTS: Throughout the study period, patients with early-onset disease (20 to 34 y) have experienced the greatest APC (20 to 34 y: 9.7; 35 to 49 y: 5.4; ≥50 y: 4.1). When APCs were stratified by disease subsite, this difference in APCs appears to be driven by midgut tumors (20 to 34 y: 19.2; 35 to 49: 8.4; ≥50 y: 3.8). Using multivariable Cox regression modeling, the following variables were associated with a higher risk of all-cause death (worse overall survival): male sex (hazard ratio [HR] 1.27; 95% confidence interval [CI]: 1.22-1.31), African American race (HR vs. white race: 1.20; 95% CI: 1.15-1.26), nonhindgut primary (HR foregut vs. hindgut primary: 2.02; 95% CI: 1.91-2.13; HR midgut vs. hindgut primary: 2.09; 95% CI: 1.95-2.24), distant disease (HR vs. regional disease: 2.06; 95% CI: 1.96-2.18), no surgery to the primary (HR: 2.34; 95% CI: 2.24-2.46), and older age (HR: 5.80; 95% CI: 4.87-6.91). CONCLUSION: Cases of early-onset well-differentiated neuroendocrine neoplasms have disproportionately increased over the past 2 decades (compared with other age groups), and this appears to have been driven mainly by midgut tumors.
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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 | 0.005 |
| 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".