Evaluation of a novel albumin platelet product (APP) fibrosis index and three non-invasive fibrosis indices in metabolic dysfunction–associated steatotic liver disease
Bibliographic record
Abstract
Background: Albumin platelet product (APP) is a novel blood-based biomarker for liver fibrosis staging. This study evaluates APP's performance against Fibrosis-4 index (FIB-4), AST–platelet ratio index (APRI), and aspartate aminotransferase–alanine aminotransferase (AST/ALT) ratio in diagnosing advanced fibrosis and cirrhosis in metabolic dysfunction–associated steatotic liver disease (MASLD) patients with and without diabetes (DM). Method: Adults with MASLD/metabolic dysfunction–associated steatohepatitis (MASH) in 2010–2023 and available fibrosis staging biomarkers were included. Clinical fibrosis staging was confirmed by liver biopsy, transient elastography (FibroScan), and/or magnetic resonance elastography. Fibrosis staging-matched fibrosis biomarkers were calculated and analyzed. Results: A total of 570 patients (48.6% male) with available clinical staging and biomarkers were analyzed. DM was present in 38% of the cohort with a significantly higher prevalence among those with advanced fibrosis or cirrhosis ( p < 0.001). APP and FIB-4 showed comparable diagnostic performance with areas under the curve (AUCs) of 0.85 (95% CI 0.82–0.88) and 0.84 (95% CI 0.81–0.87), both significantly outperforming APRI and AST/ALT ratio (AUC 0.76, p < 0.05). Importantly, all AUCs were significantly lower in the DM cohort. In patients with DM, APP outperformed FIB-4 in detecting cirrhosis (AUC 0.80 versus 0.76, p = 0.04) and was comparable for advanced fibrosis. In the non-DM cohort, APP and FIB4 performed similarly (AUCs 0.84–0.89, p > 0.05). Conclusion: APP outperformed FIB4 in detecting cirrhosis or advanced fibrosis among patients with DM and was comparable in non-DM patients. Revised FIB-4 thresholds may be needed in MASLD/MASH patients with DM to improve its diagnostic accuracy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".