Gender, Race and Disease Etiology Predict De Novo Malignancy Risk After Liver Transplantation: Insights for Future Individualized Cancer Screening Guidance
Bibliographic record
Abstract
BACKGROUND: Malignancy after liver transplant (LT) is a leading cause of mortality, but data is limited. The aim of this study was to identify patients at higher risk for de novo malignancies after LT in a large multicenter database. METHODS: The Scientific Registry of Transplant Recipients database comprising all 108 412 LT recipients across the United States between 1987 and March 2015 was analyzed with a median follow-up of 6.95 years. Potential risk factors for malignancies after LT were assessed using Cox regression analysis for the outcome of time to first malignancy. RESULTS: Mean age 51.9 ± 10.8 years, 64.6% male, 74.5% white, and 15.8% with previous malignancy. Malignancies during follow-up were 4,483 (41.3%) skin, 1519 (14.0%) hematologic, and 4842 (44.7%) solid organ. The 10-year probability of de novo malignancy was 11.5% (11.3-11.8%). On multivariable analysis, age by decade (hazard ratio [HR], 1.52; P < 0.001), male sex (HR, 1.28; P < 0.001), white race (compared with other races: HR, 1.45-2.04; P < 0.001), multiorgan transplant (HR, 1.35; P < 0.001), previous malignancy (HR, 1.34; P < 0.001), and alcoholic liver disease, autoimmune, nonalcoholic steatohepatitis (HR, 1.35; P < 0.001), and primary sclerosing cholangitis pre-LT (compared with hepatitis C virus, P < 0.001) were associated with higher risk of post-LT malignancy, but type of immunosuppression was not (P = NS). CONCLUSIONS: This large data set demonstrates the effects of ethnicity/race and etiologies of liver disease, particularly nonalcoholic steatohepatitis as additional risk factors for cancer after LT. Patients with these high-risk characteristics should be more regularly and diligently screened.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".