Trends in Serum AST-to-ALT Ratio Among U.S. Adults: Analysis of the U.S. National Health and Nutrition Examination Survey
Bibliographic record
Abstract
Background: Using nationally representative data, we examined the age-, sex-, and ethnic-specific variation in the ratio of serum aspartate aminotransferase and alanine aminotransferase (AST-to-ALT ratio or AAR) of U.S. adults (20+ years). Understanding these subgroup differences in AAR will provide insight into population patterns of these ratios, which provide a basis for normative comparisons for the application of personalized diagnostic information to patients in the clinical setting. Methods: Data for this analysis are based on continuous cycles (1999–2016) of the National Health and Nutrition Examination Survey (NHANES). Results: Within the complete sample (n = 13,731), mean AST and ALT values were similar (∼25 U/L), with higher absolute values, but lower AAR, in males compared with females. From 1999–2000 to 2015–2016 there were consistent sex, age, and ethnic differences in the AAR. Specifically, the AAR for individuals 65+ years was markedly higher in all survey years, with subtle ethnic variation [Mexican Americans (0.95–1.04) Other Hispanic (1.0–1.09), Non-Hispanic White (1.05–1.11), Non-Hispanic Black (1.12–1.22), and Other Ethnicity (1.01–1.17)]. Sex-specific analysis reveals that the lower AAR observed among Mexican Americans is almost entirely accounted for by the markedly lower AAR in men. Conclusion: Future work is necessary to understand these subgroup variations in longer term studies with incident 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".