Underestimation of liver fibrosis using vibration-controlled transient elastography on cirrhosis. Are there predictors?
Bibliographic record
Abstract
Background: Detection of liver fibrosis helps to make therapeutic decisions and with screening in patients with chronic liver diseases. Transient elastography (TE) is an accurate, noninvasive technique to assess liver fibrosis but sometimes it is underestimated. Here we aimed for associations and predictors related to the underestimation of liver fibrosis using TE. Methods: We conducted a prospective cohort study involving adult patients with cirrhosis who underwent TE and had their clinical data analyzed. Patients were categorized into two groups based on liver stiffness measurement (LSM), either ≥10 kPa or <10 kPa, which was considered an underestimation of liver fibrosis. Multivariate analysis and logistic regression models were used to identify predictors and their associated strengths. One-way analysis of variance and multiple Tukey comparisons were used to determine the association with cirrhosis etiology. Results: = 0.03), among others. The main cirrhosis etiologies included nonalcoholic fatty liver disease (30.65%), alcohol-related liver disease (27.02%), and hepatitis C virus (26.21%), with significant liver stiffness mean difference between them. There was a significant association between LSM <10 kPa and cirrhosis etiology (odds ratio 1.147; 95% CI 1.012-1.301) and ALT (odds ratio 1.019; 95% CI 1.005-1.033). Conclusions: Underestimation of liver fibrosis using TE in cirrhosis likely occurs with hepatitis C virus, nonalcoholic fatty liver disease, and low ALT levels.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".