Validation of the <scp>Fatty Liver Index</scp> for identifying non‐alcoholic fatty liver disease in a Kenyan population
Bibliographic record
Abstract
Abstract Background and Aim Fatty Liver Index (FLI) is a simple clinical scoring system estimating non‐alcoholic fatty liver disease (NAFLD). It is validated in European‐descent and Asian populations, but not in sub‐Saharan Africans. The aim of this study is to evaluate the validity of the FLI for predicting NAFLD in a population from Kenya. Methods Participants were recruited from a community‐based study conducted in Kenya. NAFLD was diagnosed using hepatic ultrasonography. Clinical, anthropometrical, biochemical and lifestyle data were obtained. The accuracy and cut‐off point of the FLI to detect NAFLD were evaluated by area under the receiver operator characteristic curve and the maximum Youden index analysis. Results A total of 640 participants (94 with NAFLD) were included. Mean age was 37.4 ± 0.4 years and 58.7% were women. Mean body mass index (BMI) was 22.3 ± 0.2 kg/m 2 and waist circumference (WC) 79.1 ± 0.4 cm. A total of 15 (2.3%) participants were diagnosed with type 2 diabetes and 65 (10.2%) with obesity (BMI ≥ 30 kg/m 2 ). AUROC of FLI for predicting NAFLD was 0.80 (95% CI 0.74–0.85), which was significantly higher compared to individual components gamma‐glutamyl transferase and triglycerides ( p < 0.05), but not compared to anthropometric parameters BMI (AUROC of 0.83, 95% CI 0.79–0.88) and WC (AUROC of 0.81, 95% CI 0.76–0.87). Conclusions FLI is a simple valid scoring system to use in rural and urban Kenyan adults. However, this index might not be superior to BMI or WC to predict NAFLD, and those measurements might therefore be more appropriate in limited settings.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".