Prevalence, risk factors and causes of discordance in fibrosis staging by transient elastography and liver biopsy
Bibliographic record
Abstract
BACKGROUND AND AIMS: Liver stiffness measurement (LSM) by transient elastography (TE) is widely used for the noninvasive assessment of fibrosis. Our objectives were to examine the prevalence, risk factors and causes of discordance between fibrosis estimated by TE and liver biopsy. METHODS: Two hundred and fifty-one patients with hepatitis B, C and nonalcoholic fatty liver disease underwent LSM by TE and liver biopsy. Predictors of discordance (≥2 fibrosis stages) between measures, which occurred in 14% of patients (n=35), were identified by comparing patient, TE and biopsy characteristics of discordant and nondiscordant cases. RESULTS: According to predefined criteria, 40% of discordances were attributed to TE error and 23% to biopsy error; 37% were indeterminate. In multivariate analysis, mild fibrosis (F0-2 vs. F3-4), and higher body mass index (BMI), ALT and LSM variability [assessed by the ratio of the interquartile range to median LSM (IQR/M)] were independently associated with discordance. Discordance was three-fold more common in patients with obesity (28 vs. 9%), ALT ≥60 U/L (20 vs. 7%) and IQR/M ≥0.17 (22 vs. 7%; all P<0.005). Based on these variables, a discordance risk score assigning 1 point to each factor was developed. The prevalence of discordance in patients with 0, 1, 2 and 3 factors were 2, 7, 20, and 55% respectively (P<0.0005). CONCLUSIONS: Discordance between liver fibrosis estimated by TE and biopsy occurs in one in seven patients. In assessing the validity of TE results, clinicians must recognize risk factors for discordance and in at-risk patients, consider alternative measures including biomarkers and possibly biopsy.
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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 | 0.003 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".