Better Prognosis of Gastric Neuroendocrine Carcinoma Than Gastric Adenocarcinoma among Whites in the United States: A Propensity Score Matching Analysis Based on SEER
Bibliographic record
Abstract
It was generally believed that the prognosis of gastric neuroendocrine carcinoma (GNEC) was worse than gastric adenocarcinoma (GAC). However, almost all previous studies compared the prognosis of GNEC and GAC based on East Asians. In this study, we evaluated the clinicopathological features and prognosis of GNEC and GAC in Whites. Patients with GNEC and GAC were identified from 2000 to 2018 in the Surveillance, Epidemiology, and End Results (SEER) database. We used propensity score matching (PSM) analysis to match the age, sex, TNM stage, and treatments received between GNEC and GAC, then compared the overall survival (OS) and cancer-specific survival (CSS) in the two types. A total of 392 cases of GNEC and 12,835 cases of GAC in Whites were recognized. After PSM, the 5-year OS rates of GNEC and GAC were 50.3% and 43.0%, respectively (p = 0.010). The 5-year CSS rates of GNEC and GAC were 57.4% and 50.1%, respectively (p = 0.012). Besides, multivariable cox regression analyses showed that GNEC was an independent predictor of improved OS (HR 0.719; 95% CI 0.607–0.853) and CSS (HR 0.691; 95% CI 0.571–0.835) in the matched data. The prognosis of GNEC was better than GAC in Whites, showing significant ethnic differences. Appropriate treatments and follow-up strategies for GNEC in Whites are probably different from East Asians. The potential genetic and molecular mechanisms need to be further explored.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".