Outcomes of small‐cell versus large‐cell gastroenteropancreatic neuroendocrine carcinomas: A population‐based study
Bibliographic record
Abstract
The recent World Health Organization classification for gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) classified poorly differentiated GEP-NENs into small cell and large cell categories. The present study aimed to assess the differences in outcomes between patients with both histological categories. The Surveillance, Epidemiology and End Results (SEER) database (1975-2016) was accessed and patients with small cell and large cell GEP-neuroendocrine carcinomas (NECs) were extracted. Differences in survival outcomes were explored through Kaplan-Meier survival estimates and multivariable Cox regression models. In total, 2204 patients with GEP-NEC were identified in the survival cohort, including 1698 patients with small cell NEC (77%) and 506 patients with large cell NEC (23%). Using Kaplan-Meier analysis/log-rank testing, large cell GEP-NEC was associated with better overall survival compared to small cell NEC (P < 0.01). Using multivariable Cox regression analysis, large cell GEP-NEC was associated with better overall survival (large cell GEP-NEC versus small cell GEP-NEC, hazard ratio = 0.77; 95% confidence interval = 0.68-0.86) and cancer-specific survival (large cell GEP-NEC versus small cell GEP-NEC, hazard ratio = 0.79; 95% 95% confidence interval = 0.69-0.91). Patients with small cell GEP-NEC have worse survival outcomes compared to those with large cell GEP-NEC. Further efforts are needed to identify biological differences and treatment sensitivities between both histological categories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".