Updated upper limit of normal for serum alanine aminotransferase value in Vietnamese population
Bibliographic record
Abstract
BACKGROUND: Alanine aminotransferase (ALT) is a marker of hepatic damage and its range can be affected by viral hepatitis, alcoholic hepatitis and non-alcoholic fatty liver diseases. We aimed to study the factors associated with higher ALT level and update the upper limit of normal (ULN) in the Vietnamese population. METHODS: This cross-sectional study enrolled 8383 adults, aged 18 years and older who visited the Medical Center at Ho Chi Minh City for a health check-up. Following the exclusion criteria, 6677 subjects were included in the analysis. RESULTS: , diastolic blood pressure >85 mm Hg, cholesterol >5.2 mmol/L, triglyceride >1.7 mmol/L, positivity, anti-hepatitis C virus positivity and fatty liver (p<0.05) were associated with higher ALT level (>40 U/L). Without considering age and gender, healthy group is defined after exclusion of participants with one of the mentioned contributing factors. The median ALT level in the healthy group was 18 in men and 13 in women. The ULN at the 95th percentile of the healthy group was 40 U/L in men and 28 U/L in women. CONCLUSION: The ULN for ALT in healthy women was lower than in healthy men. Updated ULN for ALT level can promote the identification of unhealthy subjects. More studies that involve ethnicity and lifestyle factors are needed to confirm the new ULN in the Vietnamese population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".