Supplementary Material for: Lifestyle factors and development and natural course of gastroenteropancreatic neuroendocrine tumors (GEP-NETs): a review of the literature
Bibliographic record
Abstract
Introduction: The rarity of neuroendocrine tumors (NETs) and their heterogeneous presentation, complicate the identification of risk factors for their development and natural course. Several tumor-specific prognostic factors have been identified, but less attention has been given to lifestyle factors as risk and prognostic factors. This review aims to identify studies on smoking, alcohol use, physical activity, diet body mass index (BMI) and diabetes and their association with the development and course of GEP-NETs. Methods: The literature was systematically searched for articles on lifestyle factors and NETs available via PubMed and Embase. Study quality was assessed using the Newcastle -Ottawa scale. Results: A total of 22 eligible studies out of 2915 screened articles were included. Most studies reported on smoking and alcohol, reporting conflicting results. Diet seems to have an influence on NET development, but little studies were published. Articles reporting on BMI were not unanimous on the effect on GEP-NETs. Diabetes was reported as a risk factor for NETs, whilst a protective effect was observed with metformin use. Conclusion: Different tissues, i.e., pancreas and small intestine, may respond differently to exposure to alcohol and smoking. Evidence for diet so far is too limited to draw conclusions. Diabetes seems to be an important risk factor for the development of pancreatic NET with a protective role in disease progression, while BMI is not unequivocally associated with development and prognosis of NETs. Hence, our findings suggest that lifestyle factors play an important role in NET development as disease course. Future research should consider lifestyle as an influence on disease progression and treatment response.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.475 | 0.049 |
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".