Non‐invasive algorithms for liver fibrosis: authors’ reply
Bibliographic record
Abstract
We are grateful to Yu and colleagues for their comments on our study about a large-scale comparison of three algorithms combining serum biomarkers for liver fibrosis.1, 2 We wish to clarify a few issues. First, combination algorithms of non-invasive methods for liver fibrosis have been modelled to address the main clinical end-points for decision-making in clinical practice. For instance, the SAFE biopsy algorithm for significant fibrosis (≥F2 by METAVIR) has been developed as a screening tool in patients coming for an initial evaluation. The SAFE biopsy algorithm for cirrhosis is designed for patients with a known clinical history of chronic liver disease.3 Both algorithms are intended to be clinically informative by guaranteeing to the clinician an excellent diagnostic accuracy for significant fibrosis and cirrhosis. In clinical practice, the use of transient elastography alone may be not sufficient for an accurate diagnosis of significant fibrosis.4 Moreover, the clinician should have a deep knowledge of the factors affecting the performance of transient elastography, and of the different cut-offs applied in different liver disease aetiologies and even in different studies on the same aetiology.5–9 In a recent comparative study, we have shown that SAFE biopsy and an algorithm based on the synchronous combination of transient elastography and Fibro-test have similar performance in hepatitis C.10 The choice of the algorithm may be simply based on local availability of the methods, and on the preference of the clinician. The approach of combining unrelated methods has recently received wide consensus.11–13 Second, discordant data exist in different studies regarding the superiority of transient elastography against serum biomarkers, especially for the diagnosis of significant fibrosis.5, 14–16 In agreement with Yu et al., we believe that transient elastography and serum biomarkers may be used in a complementary fashion, especially if confounding factors exist or the result of one method is unclear. Finally, even though imperfect, liver biopsy is still regarded as the gold standard of reference by international guidelines.17, 18 However, we agree that future studies should prospectively validate the non-invasive methods for liver fibrosis against clinical outcomes, including liver-related morbidity and mortality. Declaration of personal interests: GS has served as a speaker and consultant for Istituto Biochimico Italiano (IBI) and MSD, and has received research funding from Roche. AA has served as a speaker, a consultant and an advisory board member for Roche, Gilead, Novartis, BMS, J&J, MSD and Schering-Plough, and has received research funding from Gilead, MSD and BMS. Declaration of funding interests: 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 | 0.018 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.022 | 0.053 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".