Does NAFLD Fibrosis Score predict mortality risk among MAFLD patients?: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Metabolic dysfunction-Associated Fatty Liver Disease (MAFLD) is a prevalent liver disease affecting 1 million people and projected to be the leading cause of liver transplantation in 2030 due to its progression of liver fibrosis. Several non-invasive scoring systems have been established to predict advanced fibrosis among MAFLD patients, namely NAFLD Fibrosis Score (NFS). Whether its performance remains useful in determining mortality risk in the MAFLD population is less clear. This study was aimed to assess the usefulness of NFS to predict mortality risk among MAFLD patients.Method: A systematic search was conducted on Pubmed, ProQuest, and ScienceDirect from inception to February 28, 2021. All studies that met the inclusion criteria investigating MAFLD patients diagnosed with biopsy or non-biopsy were included. Bias risk was assessed using The Newcastle-Ottawa Scale. The outcome was mortality risk. We used a random-effects model, and data were pooled to determine the risk ratio (RR) and its 95% CI. Meta-analysis was conducted using Review Manager 5.3.Result: 9 cohort studies comprising 21,041 MAFLD patients were included. The risk of bias was found to be low. Pooled analysis showed that high NFS (>0.676) was significantly associated with increased mortality (RR=3.14; 95%CI=2.35-4.19; p<0.0; I2=89%). The finding was more prominent in the biopsy-proven MAFLD subgroup (RR=3.75; 95%CI=2.20-6.38; p<0.00).Conclusion: High NFS is associated with an increased risk of death in MAFLD patients.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".