Precision in Liver Diagnosis: Varied Accuracy Across Subgroups and the Need for Variable Thresholds in Diagnosis of <scp>MASLD</scp>
Bibliographic record
Abstract
BACKGROUND AND AIMS: The performance of non-invasive liver tests (NITs) is known to vary across settings and subgroups. We systematically evaluated whether the performance of three NITs in detecting advanced fibrosis in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) varies with age, sex, body mass index (BMI), type 2 diabetes mellitus (T2DM) status or liver enzymes. METHODS: Data from 586 adult LITMUS Metacohort participants with histologically characterised MASLD were included. The diagnostic performance of the Fibrosis-4 Index (FIB-4), enhanced liver fibrosis (ELF) and vibration-controlled transient elastography liver stiffness measurement (VCTE LSM) was evaluated. Performance was expressed as the area under the receiver operating characteristics curve (AUC). Thresholds for detecting advanced fibrosis (≥F3) were calculated for each NIT for fixed (high) sensitivity, specificity and predictive values. RESULTS: Differences in AUC between all subgroups were small and statistically not significant, indicating comparable performance in detecting ≥F3, irrespective of these clinical factors. However, different thresholds were needed to achieve the same level of accuracy with each test. For example, for a fixed sensitivity and specificity, the thresholds for all three NITs were higher in patients with T2DM. Effects for sex, age and liver enzymes were less pronounced. CONCLUSIONS: Performance of the selected NITs in detecting advanced liver fibrosis does not vary substantially with clinical characteristics. However, different thresholds have to be selected to achieve the same sensitivity, specificity and predictive values in the respective subgroups. Large prospective studies are called for to study NIT accuracy considering multiple patient characteristics.
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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 | 0.057 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".