Significant fibrosis in chronic liver diseases: possibilities of noninvasive screening
Bibliographic record
Abstract
The investigation aimed to study the prognostic significance of clinical and laboratory parameters, including markers of the matrix metalloproteinases (MMP) system, for the detection of significant fibrosis (F2–4) in chronic liver diseases (CLD). Seventy-six patients with CLD of viral or alcoholic etiology aged 18 to 64 years were examined. Fibrosis F0–1 was found in 36.8 % of cases, and fibrosis F2–4 – in 63.2 %. Enzyme-linked immunosorbent assay was used to determine the blood levels of MMP-1, MMP-9, and tissue inhibitors of matrix metalloproteinases-1 (TIMP-1). The ratio TIMP-1/MMP-1, TIMP-1/MMP-9 was calculated. Increased risk of fibrosis F2–4 was associated with parameters of liver stiffness ≥7.3 kPa, gamma-glutamyltranspeptidase ≥37 u/l, aspartate aminotransferase ≥53 u/l, ESR ≥8 mm/h, platelets ≤187х10⁹/l, age ≥45 years, alanine aminotransferase ≥68 u/l, TIMP-1/MMP-1 ≥36.4, albumin ≤43 g/l, total bilirubin ≥16 µmol/l, TIMP-1 ≥501 ng/ml, international normalized ratio ≥1.08, alkaline phosphatase ≥112 u/l, with the presence of moderate/severe cytolysis. According to multivariate logistic regression, liver fibrosis F2–4 was associated with blood levels of TIMP-1 and platelets, values of ESR. The combination of these parameters had a sensitivity of 70.8 % and a specificity of 100 % in the prediction of fibrosis F2–4. Thus, the levels of TIMP-1 and platelets in the blood and ESR are independent risk factors for significant fibrosis in CLD due to their participation in hepatic fibrogenesis
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".