Non-Alcoholic Fatty Liver Disease in Africa and Middle East: An Attempt to Predict the Present and Future Implications on the Healthcare System
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) encompasses a group of hepatic diseases that range in severity. NAFLD is increasingly recognized as an epidemic among different populations, including those in Africa and the Middle East. The objective of this narrative review is to document the prevalence of and risk factors for NAFLD in Africa and the Middle East and the potential implications on the healthcare systems. An in-depth search on Google Scholar, Medline and PubMed was conducted using the terms "non-alcoholic fatty liver disease" and "non-alcoholic steatohepatitis", in addition to "prevalence and risk factors for NAFLD", with special emphasis on Africa and the Middle East countries. There were three types of epidemiological studies that included prevalence, risk factors and management/complications of NAFLD. There was noticeable variation in the prevalence of NAFLD among different countries, based on the variation in the prevalence of risk factors (type 2 diabetes, obesity, metabolic syndrome and dyslipidemia) and the diagnostic tool used in the study. However, the highest prevalence rate was reported in some Middle East countries. In Africa, there were few studies about NAFLD and most reported variable prevalence rates. There is an increasing prevalence of NAFLD as a result of the increasing risk factors, particularly in the Middle East, while in Africa, the situation is still unclear. Health providers in these regions are faced with many challenges that need urgent plans.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".