Impact of statin treatment on non-invasive tests based predictions of fibrosis in a referral pathway for NAFLD
Bibliographic record
Abstract
OBJECTIVE: In non-alcoholic fatty liver disease (NAFLD), fibrosis determines the risk of liver complications. Non-invasive tests (NITs) such as FIB-4, NAFLD Fibrosis Score (NFS) and Hepamet, have been proposed as a tool to triage NAFLD patients in primary care (PC). These NITs include AST±ALT in their calculations. Many patients with NAFLD take statins, which can affect AST/ALT, but it is unknown if statin affects NITs fibrosis prediction. METHODS: We included 856 patients referred through a standardised pathway from PC with a final diagnosis of NAFLD. 832 had reliable vibration controlled transient elastography (VCTE) measurements. We assessed the effects of statins on the association between NITs and VCTE at different fibrosis thresholds. RESULTS: 129 out of 832 patients were taking a statin and 138 additional patients had indication for a statin. For any given FIB-4 value, patients on a statin had higher probabilities of high VCTE than patients not on a statin. Adjusting for body mass index, diabetes and age almost completely abrogated these differences, suggesting that these were related to patient's profile rather to a specific effect of statins. Negative predictive values (NPVs) of FIB-4 <1.3 for VCTE >8, 10, 12 and 16 were, respectively, 89, 94, 96% and 100% in patients on a statin and 92, 95, 98% and 99% in patients not on a statin. Statins had similar impact on Hepamet predictions but did not modify NFS predictions. CONCLUSION: In patients with NAFLD referred from PC, those on statins had higher chances of a high VCTE for a given FIB-4 value, but this had a negligible impact on the NPV of the commonly used FIB-4 threshold (<1.3).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".