Metabolic dysfunction-associated steatotic liver disease: The question of long-term high-normal alanine aminotransferase as a screening test
Bibliographic record
Abstract
The growing prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is being driven by the obesity epidemic. The quest for solutions continues particularly with regard to early detection. This editorial comments on the utility of long-term high-normal alanine aminotransferase (ALT) in screening for MASLD. Chen et al found that new onset MASLD can be detected by repetitively high normal ALT. Implicit in this concept is the question of what should be the accepted upper limit of normal (ULN) for ALT. It was previously set at 40 IU/L based on studies that included people with subclinical liver disease but the new consensus is 30/19 U/L in healthy males/females. Thus, when Chen et al defines the ULN as 40 U/L, others may view it as excessively high. It is important to recognize the variables affecting ULN e.g. instrumentation, diurnal variations, exercise and ageing. These variables matter when the distinctions are subtle e.g. normal vs high-normal. In this regard, the utility of long-term high normal ALT as a disease marker could be enhanced by combining it with other biomarkers, imaging and MASLD genetics to create machine learning classifiers. All in all, Chen et al ’s work on long-term high normal ALT as a marker of new-onset MASLD deserves merit.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".