Non-Alcoholic Fatty Liver Disease in Africa and Middle East: An Attempt to Predict the Present and Future Implications on the Healthcare System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it