Alcoholic liver disease is a strong predictor of colorectal polyps in liver transplant recipients
Bibliographic record
Abstract
Abstract Background and aims Colorectal cancer (CRC) is associated with a significantly reduced survival rate in transplant recipients. The prevalence and risk factors of CRC and of colorectal polyps after orthotopic liver transplant (OLT) remain unclear. The study aim was to determine the prevalence of colorectal polyps in OLT recipients. A secondary objective was to explore possible risk factors of polyps. Patients and materials This was a retrospective single center study of all OLT recipients transplanted between 2007 and 2009. All patients who underwent a colonoscopy 5 ± 5 years after OLT were included. The outcome was colorectal polyps, as identified on colonoscopy. A logistic regression model was performed to identify potential predictors of polyps. Results Of 164 OLT recipients, 80 were included in this study. Polyps were diagnosed in 37 % of patients before transplant and in 33 % afterwards. With regard to post-transplant lesions, 22 % were advanced adenomas or cancerous. In the regression analysis, the odds of post-transplant polyps were 11 times higher in patients with alcoholic liver disease (OR 11.3, 95 %CI 3.2 – 39.4; P < 0.001). Conclusion Patients with end-stage liver disease may be at high risk of colorectal polyps before and after liver transplant, and screening should be continued in both contexts. Those with alcoholic liver disease are particularly at risk for post-OLT polyps and may benefit from more intensive screening.
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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) | 0.003 | 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".