Metabolic Associated Fatty Liver Disease in Indigenous Arctic Populations: A Systematic Review Emphasizing Awareness of Minorities
Bibliographic record
Abstract
Aim: Non-Alcoholic Fatty Liver Disease (NAFLD) is a leading cause of chronic liver disease affecting 25% of the World population. However, the prevalence is only sparsely investigated in circumpolar areas. We aimed to perform a systematic review of the literature to estimate the prevalence of MAFLD in indigenous arctic populations. Material and methods: This systematic literature review adheres to the PRISMA guidelines and the Cochrane Handbook. We included trials estimating the prevalence of NAFLD or MAFLD in the related indigenous arctic populations of Inuit, Alaska Natives, Inupiat, and Yupik residing in Greenland and Alaska, as well as Arctic Canadian territories and the Chukotka Okrug of Russia. Results: We identified 1105 unique references for screening and five studies qualified for inclusion. Four of the studies examined Alaskan Natives either with self-identification as Inupiat, Yupik, Athabascan Indian, Southeast Alaska Indian, or Aleut or in combination with other Native Americans. Only one study examined MAFLD in Greenland Inuit. The prevalence of MAFLD varied between 21% and 65%, with a considerable risk of bias from other coexisting liver diseases. One study, using the non-invasive FIB-4 score (>1.45), estimated the prevalence of liver fibrosis to be 16%, while another reported that 8% of patients with MAFLD had cirrhosis. Conclusion: In this first systematic review on the prevalence of MAFLD in arctic indigenous populations related to Inuit we showed a MAFLD prevalence of 21-65% with 16% having fibrosis. This may emphasize the need for health research of MALFD in minority populations for early detection and improving patient outcome and also taking into account demographic and cultural differences.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".