Sex-Based Differences in Prognosis of Patients With Gastroenteropancreatic-Neuroendocrine Neoplasms
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to assess sex-based differences in prognosis of a contemporary cohort of gastroenteropancreatic-neuroendocrine neoplasm (GEP-NEN) patients. METHODS: Surveillance, Epidemiology, and End Results database was accessed, and cases with GEP-NENs were selected. Rates of GEP-NEN diagnosis from 1975 to 2016 for both male patients and female patients were reviewed. Survival outcomes of GEP-NEN patients diagnosed from 2010 to 2014 were determined through Kaplan-Meier estimates and multivariable Cox regression analysis. Overall survival analyses were stratified by stage and histology. RESULTS: A total of 20,836 GEP-NEN patients were diagnosed from 2010 to 2014, and they were included in the current analysis. These include 10,336 male patients and 10,500 female patients. Annual percent change for the age-adjusted rate for GEP-NENs in the United States (1975-2016) is 5.0 (95% confidence interval [CI], 4.8-5.2). When stratified by sex, annual percent change for male patients was 4.8 (95% CI, 4.6-5.1), whereas for female patients, it was 5.0 (95% CI, 4.8-5.3). Female patients have better overall survival compared with male patients among all substrata of patients (according to stage, histology, and differentiation) (P for all comparisons <0.01). CONCLUSIONS: Female sex seems to be associated with better overall survival among patients with GEP-NENs. It is unclear if this is the result of differences in noncancer mortality or is the result of inherent biological differences.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".