Non-invasive assessment of liver fibrosis: it is time for laboratory medicine
Bibliographic record
Abstract
Chronic liver diseases (CLDs) represent a major cause of morbidity and mortality worldwide. In all etiologies of CLDs, staging of liver fibrosis is essential for both prognosis and management. Until a few years ago, liver biopsy was the only tool for the diagnosis of liver fibrosis in patients with CLDs. However, liver biopsy is an invasive and costly procedure. More recently, various serum biomarkers and laboratory tests have been proposed as surrogates of liver histology. Due to inadequate diagnostic accuracy or to lack of sufficient validation, guidelines still do not recommend them as a substitute for liver biopsy that is still considered the gold standard for the diagnosis of liver fibrosis. Notably, non-invasive serum biomarkers, when combined, may reduce by 50%-80% the number of liver biopsies needed for correctly classifying hepatic fibrosis. However, liver biopsy cannot be avoided completely, but should be used in those cases in which non-invasive methods show poor accuracy. In this view, serum biomarkers and liver biopsy represent a union between laboratory medicine and hepatology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".