Prevalence of non-alcoholic fatty liver disease among Iranians aged 6 to 18 years: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) can lead to serious complications and have adverse effects on physical and mental well-being in young people. This review aimed to investigate the prevalence of NAFLD among Iranians aged 6 to 18 years. Materials and Methods: This study was conducted by systematic review and meta-analysis methods. A detailed search was performed on various international and Iranian databases from January 2000 to January 2023. The international databases included Pubmed, Scopus, Embase, and Web of Science, while the Iranian databases consisted of MagIran and SID. The quality of the selected studies was assessed using the Newcastle-Ottawa Scale. Overall prevalence was estimated using the random-effects model and DerSimonian and Laird criteria with a 95% confidence interval. The Q-Cochrane test and the I2 index were used to assess heterogeneity between studies. In addition, a sensitivity analysis was performed to ensure the reliability of the results. Data analysis was performed in Stata12 software. Results: Finally, 9 studies were selected for analysis, of which, 7 studies were of good quality, while 2 studies were of average quality based on the assigned scores. According to the random-effects model, the overall prevalence of NAFLD in Iranian individuals aged 6 to 18 years was 35% with a 95% confidence interval (24% to 46%). Conclusion: The results of our study revealed a high prevalence of NAFLD in Iranian individuals aged 6 to 18 years. Policymakers and healthcare planners in Iran must implement educational programs aimed at the prevention and early diagnosis of this disease.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".