Bibliographic record
Abstract
Recent epidemiological evidence indicates a significant rise in cirrhosis burden over the past 2 decades in all parts of the world, with cirrhosis incidence rates and related deaths escalating quickly. Women face unique risk factors and susceptibility to chronic liver diseases compared with men, underscoring the need for a sex-specific approach in early identification, reversal of causative factors, and complication prevention. This review aims to explore epidemiological trends and sex-specific factors contributing to the global epidemiology of cirrhosis among female patients today. While cirrhosis prevalence remains higher in male patients globally, the incidence rate from 2010 to 2019 grew faster among female patients. The female-to-male incidence ratio of metabolic dysfunction-associated steatotic liver disease-related cirrhosis globally in 2019 was 1.3, indicating a shifting trend toward new diagnoses among women now surpassing that of men. Alcohol-associated cirrhosis epidemiology is also changing, with trends toward an equal incidence of alcohol-associated cirrhosis between both sexes, particularly in industrialized nations with increased alcohol accessibility. Cirrhosis from viral hepatitis remains the main etiology among female patients in endemic regions. Sex differences in epidemiology are likely multifactorial, influenced by varying risk factors, susceptibility, and behaviors between sexes. Further research is necessary to better understand these disparities and to tailor sex-specific interventions toward improved management and treatment strategies, ultimately enhancing outcomes for women with cirrhosis and providing better patient-centered care.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".