Comparing the performance of Fibrosis-4 and Non-Alcoholic Fatty Liver Disease Fibrosis Score with transient elastography scores of people with non-alcoholic fatty liver disease
Bibliographic record
Abstract
Background: With the rate of non-alcoholic fatty liver disease (NAFLD) on the rise, the necessity of identifying patients at risk of cirrhosis and its complications is becoming ever more important. Liver biopsy remains the gold standard for assessing fibrosis, although costs, risks, and availability prohibit its widespread use with at-risk patients. Transient elastography has proven to be a non-invasive and accurate way of assessing fibrosis, although the availability of this modality is often limited in primary care settings. The Fibrosis-4 (FIB-4) and Non-Alcoholic Fatty Liver Disease Fibrosis Score (NFS) are scoring systems that incorporate commonly measured lab parameters and BMI to predict fibrosis. Method: In this study, we compared FIB-4 and NFS scores with transient elastography scores to assess the accuracy of these inexpensive and readily available scoring systems in detecting fibrosis. Results: Using an NFS score cut-off of -1.455 and a FibroScan score cut-off of ≥8.7 kPa, the NFS score had a negative predictive value of 94.1%. Using a FibroScan score cut-off of ≥8.7 kPa, the FIB-4 score had a negative predictive value of 91.6%. Conclusion: The NFS and FIB-4 are non-invasive, inexpensive scoring systems that have high negative predictive value for fibrosis compared with transient elastography scores. These findings suggest that the NFS and FIB-4 can provide adequate reassurance to rule out fibrosis in patients with NAFLD and can be used with select patients to circumvent the need for transient elastography or liver biopsy.
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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 | 0.002 | 0.007 |
| 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) | 0.002 | 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".