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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".