Hepatitis C-related cirrhosis: A predictor of diabetes after liver transplantation
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection has recently been suggested to be a risk factor for the development of diabetes mellitus. The aim of our study was to investigate whether the prevalence of diabetes is increased among liver transplant recipients infected with HCV. We compared the prevalence of diabetes among 278 liver transplant recipients whose original cause of liver failure was HCV infection (110 patients), hepatitis B virus infection (HBV; 53 patients), and cholestatic liver disease (CLD; 115 patients). The pretransplantation prevalence of diabetes was higher in the HCV group (29%) compared with the HBV (6%) and CLD (4%) groups (P <.001). The prevalence of diabetes remained higher in the HCV group 1 year after transplantation: 37%, 10%, and 5% in the HCV, HBV, and CLD groups, respectively (P <.001). The cumulative steroid dose during the first year of transplantation was significantly lower in the HCV group compared with the CLD group. Multivariate analysis revealed that HCV-related liver failure (P =.002), pretransplantation diabetes (P <.0001), and male sex (P =.019) were independent predictors of the presence of diabetes 1 year after transplantation. The high prevalence of diabetes persisted in the HCV group, with 41% diabetic at 5 years. The majority of patients with diabetes mellitus (89%) required insulin therapy after transplantation. Patient and graft survival rates were similar among patients with and without diabetes. In conclusion, our study shows that there is a high prevalence of diabetes among liver transplant recipients infected with HCV both before and after transplantation.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".