Non-invasive diagnosis of advanced fibrosis and cirrhosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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) | 0.004 | 0.001 |
| 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) | 0.000 | 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 it