Clinicopathological Features and Prognosis Analysis of Primary Bile Duct and Ampullary Neuroendocrine Neoplasms: A Population-Based Study from 1975 to 2016
Bibliographic record
Abstract
BACKGROUND: The main purpose of this study is to analyze the clinicopathological features and prognosis factors of bile duct and ampullary neuroendocrine neoplasms (NENs). METHODS: The relevant data were collected from the SEER database from 1975 to 2016. The Kaplan-Meier curve and Cox model were used for survival analysis. The nomogram was drawn to predict the survival rate. The calibration, discrimination and clinical utility of the nomogram were evaluated by calibration curve, the concordance index (C-index) and decision curve analysis (DCA). RESULTS: A total of 340 cases were included in our research. According to Kaplan-Meier analysis, 1-year, 3-year and 5-year of overall survival (OS) were 77.3%, 61.9% and 58.4%, while 1-year, 3-year and 5-year of the disease-specific survival (DSS) were 82.7%, 69.3% and 66.9%, respectively. The multivariable analysis results showed that age, histological grade, SEER stage and surgery were independent predictors for either OS or DSS. The calibration curve and the C-index value indicated that the nomogram was well calibrated and had good discrimination. DCA showed that the model had ideal net benefits. CONCLUSIONS: The age, histological grade, SEER stage and surgery were identified as independent prognostic variables for OS and DSS. After verification, nomogram has good predictive ability and clinical application value.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".