Evaluation of the fibrosis‐4 index for detection of advanced fibrosis among individuals at risk for intestinal failure–associated liver disease
Bibliographic record
Abstract
BACKGROUND: Intestinal failure-associated liver disease (IFALD) refers to the spectrum of liver injury secondary to IF and parenteral nutrition use. Our aim was to evaluate the use of noninvasive indices of liver fibrosis to detect advanced fibrosis among individuals at risk for IFALD. METHODS: We performed a secondary analysis of a retrospective study, including all liver biopsies performed on individuals undergoing intestinal transplantation (ITx) between January 2000 and May 2014. To determine the clinical utility of detecting advanced fibrosis, receiver operating characteristic curves were developed. Comparison between the area under the curves was performed by DeLong test. RESULTS: Fifty-three patients had a liver biopsy performed at the time of ITx; 13 of 53 (24.5%) patients had advanced fibrosis. The fibrosis-4 (FIB-4) index positively correlated to the stage of fibrosis on liver biopsy (r = 0.426, P = .002). When compared against the FIB-4 index, the aspartate aminotransferase to platelet ratio index had a significantly decreased ability to correctly identify the presence of advanced fibrosis (P = .019). When determining the cutoff value with 90% specificity for the detection of advanced fibrosis, a FIB-4 index of ≥4.4 had a sensitivity of 0.462 and a positive predictive value of 0.6. CONCLUSION: In this retrospective cohort study, we found a positive correlation between the FIB-4 index and the liver fibrosis stage as characterized by the Brunt classification. This evaluation of the FIB-4 index against liver biopsies supports the use of the FIB-4 index in the detection of liver fibrosis in IF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".