Development and validation of nonalcoholic fatty liver disease test: a simple sensitive and specific marker for early diagnosis of nonalcoholic fatty liver disease
Bibliographic record
Abstract
AIM: This study aimed to develop a noninvasive test for identifying patients with nonalcoholic fatty liver disease (NAFLD) based on clinical and routine laboratory data. METHODS: The developed model 'NAFLD test' was compared to the most commonly used NAFLD scores and then validated in three groups of NAFLD patients from five centers in Egypt, China, and Chile. Patients were divided into the discovery cohort (n = 212) and the validation study (n = 859). The ROC curve and stepwise multivariate discriminant analysis were used to develop and validate the NAFLD test and evaluate its diagnostic performance, which was then compared to other NAFLD scores. RESULTS: Elevated C-reactive protein (CRP), cholesterol, BMI, and alanine aminotransferase (ALT) levels were significantly associated with NAFLD (P < 0.0001). NAFLD test is depicted as (-0.695 + 0.031 × BMI + 0.003 × cholesterol + 0.014 × ALT + 0.025 × CRP) to discriminate patients with NAFLD from healthy individuals. The area under the ROC curve (AUC) of the NAFLD test was 0.92 [95% confidence interval (CI): 0.88-0.96]. The NAFLD test was the most accurate diagnostic indicator of NAFLD when compared to widely used NAFLD indices. Upon validating the NAFLD test, its AUC (95% CI) for distinguishing patients with NAFLD from healthy individuals was 0.95 (0.94-0.97), 0.90 (0.87-0.93), and 0.94 (0.91-0.97) in Egyptian, Chinese, and Chilean patients with NAFLD respectively. CONCLUSION: The NAFLD test is a new validated diagnostic biomarker that can be utilized for the early diagnosis of NAFLD with high diagnostic performance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".