A188 COMPARING THE PERFORMANCE OF FIBROSIS-4 (FIB-4) AND NON-ALCOHOLIC FATTY LIVER DISEASE FIBROSIS SCORE (NFS) WITH FIBROSCAN SCORES IN NON-ALCOHOLIC FATTY LIVER DISEASE
Bibliographic record
Abstract
Abstract Background With the rates 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 the costs, risks, and availability prohibit its widespread use for at-risk patients. Fibroscan has proven to be a non-invasive and accurate way of assessing fibrosis, although the availability of this modality is often limited in the primary care setting. The Fibrosis-4 (FIB-4) and Non-Alcoholic Fatty Liver Disease Fibrosis Score (NFS) are scoring systems which incorporate commonly measured lab parameters and BMI to predict fibrosis. In this study, we compared FIB-4 and NFS values to fibroscan scores to assess the accuracy of these inexpensive and readily available scoring systems for detecting fibrosis. Aims The aim of this study was to determine if non-invasive and inexpensive scoring systems (FIB-4 and NFS) can be used to rule out fibrosis in non-alcoholic fatty liver disease with comparable efficacy to fibroscan. Ultimately, we aim to demonstrate that these scoring systems can be used as an alternative to fibroscan in some patients. Methods Data was collected from 317 patient charts from the Vancouver General Hepatology Clinic. 93 patients were removed from the study due to insufficient data (missing Fibroscan score or lab work necessary for FIB-4/NFS). For the remaining 224 patients, FIB-4 and NFS were calculated and compared to fibrosis scores both independently and in combination. Results: Using a NFS score cut-off of -1.455 and a fibroscan score cut-off of ≥8.7kPa, the NFS had a sensitivity of 71.9%, a specificity of 75%, and a negative predictive value of 94.1%. For a fibroscan score cut-off of ≥8.0kPa, the NFS had a sensitivity of 66.7%, a specificity of 75.7%, and a negative predictive value of 91.5%. Using a fibroscan score cut-off of ≥8.7kPa, the FIB-4 score had a sensitivity of 53.1%, specificity of 84.9%, and a negative predictive value of 91.6%. For a cut-off of ≥8.0kPa, it had a sensitivity of 51.3%, and 85.9%, and a negative predictive value of 89.3%. Conclusions: The NFS and FIB-4 are non-invasive scoring systems that have high sensitivity and negative predictive value for fibrosis when compared to fibroscan scores. These findings suggest that the NFS and FIB-4 can provide adequate reassurance to rule-out fibrosis in select patients, and has promising use in the primary care setting where fibroscan access is often limited. Funding Agencies None
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".