Bibliographic record
Abstract
A common characteristic of all chronic liver diseases is the occurrence and progression of fibrosis toward cirrhosis. Consequently, liver fibrosis assessment plays an important role in hepatology. Besides its importance for prognosis, determining the level of fibrosis reveals the natural history of the disease and the risk factors associated with its progression, to guide the antifibrotic action of different treatments. Currently, in clinical practice, there are three available methods for the evaluation of liver fibrosis: liver biopsy, which is still considered to be the 'gold standard'; serological markers of fibrosis and their mathematical combination - suggested in recent years to be an alternative to liver biopsy - and, more recently, transient elastography (TE). TE is a new, simple and noninvasive method used to measure liver stiffness. This technique is based on the progressing speed of an elastic shear wave within the liver. Currently, there are only a few studies that have evaluated TE effectiveness in chronic liver diseases, mostly in patients infected with the hepatitis C virus. Further studies are needed in patients with chronic liver disease, to assess the effectiveness of the fibrosis treatment.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".