Gut inflammation and tumorigenesis: every site has a different tale to tell
Bibliographic record
Abstract
Gut inflammation has been correlated with cancerogenesis by disrupting gastrointestinal homeostasis. Numerous chronic inflammatory disorders of the tubular gastrointestinal tract (e.g., gastroesophageal reflux disease, Helicobacter pylori-induced and autoimmune chronic gastritis, celiac disease, and inflammatory bowel diseases) have been variably associated with an increased neoplastic risk. Gastrointestinal inflammation-induced neoplasms include epithelial tumors (esophageal squamous cell carcinoma and adenocarcinoma, gastric adenocarcinoma and neuroendocrine tumors, small bowel adenocarcinoma and neuroendocrine tumors, and colorectal cancer) and lymphomas (such as gastric marginal zone lymphomas and enteropathy-associated T cell lymphoma). In the last decades, numerous studies have investigated the pathogenetic mechanisms and the microenvironmental/microbiome changes that trigger genetic and/or epigenetic alterations eventually leading to tumorigenesis, often through a histologically recognizable inflammation-dysplasia-carcinoma cancerogenic sequence. In the present review, an overview of the current knowledge on the links between inflammatory diseases and neoplasms of the tubular GI tract, applying a site-by-site approach, is provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".