Incidence of Luminal Gastrointestinal Cancers in Patients with Cirrhosis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: The global incidence of cirrhosis and luminal gastrointestinal cancers are increasing. It is unknown if cirrhosis itself is a predisposing factor for luminal gastrointestinal cancer. Aims: The aim of our study was to investigate the incidence of luminal gastrointestinal cancers in patients with underlying cirrhosis. Methods: An electronic search was conducted to study the incidence of luminal gastrointestinal cancers in patients with cirrhosis. Study-specific standardized incidence ratios (SIR) along with corresponding 95% confidence intervals for both overall cancer incidence and luminal cancer incidence were analyzed using a random-effects model. Subgroup analysis was performed based on cirrhosis etiology and location of luminal malignancy. Results: We identified 5054 articles; 4 studies were selected for data extraction. The overall incidence of all cancers was significantly higher in patients with cirrhosis, with an SIR of 2.79 (95% CI 2.18–3.57). When stratified by cirrhosis etiology, the incidence of luminal cancers remained significantly elevated for alcohol (SIR 3.13, 95% CI 2.24–4.39), PBC (SIR 1.40, 95% CI 1.10–1.79), and unspecified cirrhosis (SIR 3.52, 95% CI 1.87–6.65). Conclusion: The incidence of luminal gastrointestinal cancer is increased amongst patients with cirrhosis. Therefore, increased screening of luminal cancers, and in particular these upper luminal tract subtypes, should be considered in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".