Risk of Colorectal Carcinoma in Post-Liver Transplant Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Liver transplant patients (LTx) have an increased risk for developing de novo malignancies, but for colorectal cancer (CRC) this risk is less clear. We aimed to determine whether the CRC risk post-LTx was increased. A systematic search was performed in MEDLINE and Cochrane databases to identify studies published between 1986 and 2008 reporting on the risk of CRC post-LTx. The outcomes were (1) CRC incidence rate (IR per 100,000 person-years (PY)) compared to a weighted age-matched control population using SEER and (2) relative risk (RR) for CRC compared to the general population. If no RR data were available, the RR was estimated using SEER. Twenty-nine studies were included. The overall post-LTx IR was 119 (95% CI 88-161) per 100,000 PY. The overall RR was 2.6 (95% CI 1.7-4.1). The non-primary sclerosing cholangitis (PSC) IR was 129 per 100,000 PY (95% CI 81-207). Compared to SEER (71 per 100,000 PY), the non-PSC RR was 1.8 (95% CI 1.1-2.9). In conclusion, the overall transplants and the subgroup non-PSC transplants have an increased CRC risk compared to the general population. However, in contrast to PSC, non-PSC transplants do not need an intensified screening strategy compared to the general population until a prospective study further defines recommendations.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".