A Precision Medicine Guided Approach to the Utilization of Biomarkers in MASLD
Bibliographic record
Abstract
The new nomenclature of metabolic dysfunction-associated steatotic liver disease (MASLD) emphasizes a positive diagnosis based on cardiometabolic risk factors. This definition is not only less stigmatizing but also allows for subclassification and stratification, thereby addressing the heterogeneity of what was historically referred to as nonalcoholic fatty liver disease. The heterogeneity within this spectrum is influenced by several factors which include but are not limited to demographic/dietary factors, the amount of alcohol use and drinking patterns, metabolic status, gut microbiome, genetic predisposition together with epigenetic factors. The net effect of this dynamic and intricate system-level interaction is reflected in the phenotypic presentation of MASLD. Therefore, the application of precision medicine in this scenario aims at complex phenotyping with consequent individual risk prediction, development of individualized preventive strategies, and improvements in the clinical trial designs. In this review, we aim to highlight the importance of precision medicine approaches in MASLD, including the use of novel biomarkers of disease, and its subsequent utilization in future study designs.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".