Changing trends in the etiology of liver transplantation in Turkiye: A multicenter study
Bibliographic record
Abstract
Background and Aim: This study aimed to identify the indications for liver transplantation (LT) based on underlying etiology and to characterize the patients who underwent LT. Materials and Methods: We conducted a multicenter cross-sectional observational study across 11 tertiary centers in Turkiye from 2010 to 2020. The study included 5,080 adult patients. Results: The mean age of patients was 50.3±15.2 years, with a predominance of female patients (70%). Chronic viral hepatitis (46%) was the leading etiological factor, with Hepatitis B virus infection at 35%, followed by cryptogenic cirrhosis (24%), Hepatitis C virus infection (8%), and alcohol-related liver disease (ALD) (6%). Post-2015, there was a significant increase in both the number of liver transplants and the proportion of living donor liver transplants (p<0.001). A comparative analysis of patient characteristics before and after 2015 showed a significant decline in viral hepatitis-related LT (p<0.001), whereas fatty liver disease-related LT significantly increased (p<0.001). Conclusion: Chronic viral hepatitis continues to be the primary indication for LT in Turkiye. However, the proportions of non-alcoholic fatty liver disease (NAFLD) and ALD-related LT have seen an upward trend over the years.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".