Cirrhosis etiology trends in developing countries: Transition from infectious to metabolic conditions. Report from a multicentric cohort in central Mexico
Bibliographic record
Abstract
Background: Cirrhosis is a public health threat associated with high mortality. Alcoholic Liver Disease (ALD) is the leading cause in Latin America and Metabolic Associated Fatty Liver Disease (MAFLD) in western countries. In Mexico, ALD and chronic Hepatitis C Virus infection (HCV) were the most frequent aetiologies during the past decades. We aimed to describe the trends in the aetiologies of cirrhosis in a middle-income country. Methods: We performed a retrospective cohort study including patients diagnosed with cirrhosis between 2000 and 2019 from six different tertiary care hospitals in central Mexico. We collected information regarding cirrhosis etiology, year of diagnosis, hepatocellular carcinoma development, liver transplantation, and death. We illustrated the change in the tendencies of cirrhosis aetiologies by displaying the proportional incidence of each etiology over time stratified by age and gender, and we compared these proportions over time using chi square tests. Findings: Overall, 4,584 patients were included. In 2019, MAFLD was the most frequent cirrhosis etiology (30%), followed by ALD (24%) and HCV (23%). During the study period, MAFLD became the leading etiology, ALD remained second, and HCV passed from first to fourth. When analysed by gender, ALD was the leading etiology for men and MAFLD for women. The annual incidence of HCC was 3·84 cases/100 persons-year, the median survival after diagnosis was 12·1 years, and seven percent underwent LT. Interpretation: Increased alcohol consumption and the obesity epidemic have caused a transition in the aetiologies of cirrhosis in Mexico. Public health policies must be tailored accordingly to mitigate the burden of alcohol and metabolic conditions in developing countries. Funding: None.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".