Signs of non-alcoholic fatty liver disease in indigenous Arctic populations - a systematic review.
Bibliographic record
Abstract
INTRODUCTION: The increasing prevalence of obesity and type 2 diabetes mellitus has become a global healthcare concern spreading to indigenous Arctic populations. As non-alcoholic fatty liver disease (NAFLD) is strongly associated with the metabolic syndrome, it has become a leading cause of chronic liver disease. However, data are sparse on the prevalence of NAFLD in indigenous Arctic populations who may have a different risk profile for diabetes complications. METHODS: We conducted a systematic review to estimate the prevalence of NAFLD or signs of NAFLD in indigenous Arctic people inhabiting Greenland, Alaska, Canadian territories and Eastern Russia. Also, we wanted to discuss how Arctic research in metabolic disease such as NAFLD may move forward. RESULTS: Through the pre-specified search of Ovid MEDLINE and Embase, 3,070 unique references were identified and six studies including 5,487 persons qualified for data extraction. The prevalence of NAFLD or signs of NAFLD varied between 21% and 65%. The risk of bias was considerable particularly due to the inclusion of small and heterogeneous studies. CONCLUSION: Only limited published research exists on NAFLD in indigenous Arctic populations. This review reports that the prevalence of NAFLD or signs of NAFLD in the indigenous Arctic populations residing in Arctic Regions may be similar to the global level, emphasising the need for further health research in indigenous Arctic populations.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".