Bibliographic record
Abstract
Liver cirrhosis is a common and growing public health problem globally. The diagnosis of cirrhosis portends an increased risk of morbidity and mortality. Liver biopsy is considered the gold standard for diagnosis of cirrhosis and staging of fibrosis. However, despite its universal use, liver biopsy is an invasive and inaccurate gold standard with numerous drawbacks. In order to overcome the limitations of liver biopsy, a number of non-invasive techniques have been investigated for the assessment of cirrhosis. This review will focus on currently available non-invasive markers of cirrhosis. The evidence behind the use of these markers will be highlighted, along with an assessment of diagnostic accuracy and performance characteristics of each test. Non-invasive markers of cirrhosis can be radiologic or serum-based. Radiologic techniques based on ultrasound, magnetic resonance imaging and elastography have been used to assess liver fibrosis. Serum-based biomarkers of cirrhosis have also been developed. These are broadly classified into indirect and direct markers. Indirect biomarkers reflect liver function, which may decline with the onset of cirrhosis. Direct biomarkers, reflect extracellular matrix turnover, and include molecules involved in hepatic fibrogenesis. On the whole, radiologic and serum markers of fibrosis correlate well with biopsy scores, especially when excluding cirrhosis or excluding fibrosis. This feature is certainly clinically useful, and avoids liver biopsy in many cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".