The Feasibility and Reliability of Transient Elastography Using Fibroscan<sup>®</sup>: A Practice Audit of 2335 Examinations
Bibliographic record
Abstract
BACKGROUND: Liver stiffness measurement (LSM) using transient elastography is widely used in the management of patients with chronic liver disease. OBJECTIVES: To examine the feasibility and reliability of LSM, and to identify patient and operator characteristics predictive of poorly reliable results. METHODS: The present retrospective study investigated the frequency and determinants of poorly reliable LSM (interquartile range [IQR]⁄median LSM [IQR⁄M] >30% with median liver stiffness ≥7.1 kPa) using the FibroScan (Echosens, France) over a three-year period. Two experienced operators performed all LSMs. Multiple logistic regression analyses examined potential predictors of poorly reliable LSMs including age, sex, liver disease, the operator, operator experience (<500 versus ≥500 scans), FibroScan probe (M versus XL), comorbidities and liver stiffness. In a subset of patients, medical records were reviewed to identify obesity (body mass index ≥30 kg⁄m2). RESULTS: Between July 2008 and June 2011, 2335 patients with liver disease underwent LSM (86% using the M probe). LSM failure (no valid measurements) occurred in 1.6% (n=37) and was more common using the XL than the M probe (3.4% versus 1.3%; P=0.01). Excluding LSM failures, poorly reliable LSMs were observed in 4.9% (n=113) of patients. Independent predictors of poorly reliable LSM included older age (OR 1.03 [95% CI 1.01 to 1.05]), chronic pulmonary disease (OR 1.58 [95% CI 1.05 to 2.37), coagulopathy (OR 2.22 [95% CI 1.31 to 3.76) and higher liver stiffness (OR per kPa 1.03 [95% CI 1.02 to 1.05]), including presumed cirrhosis (stiffness ≥12.5 kPa; OR 5.24 [95% CI 3.49 to 7.89]). Sex, diabetes, the underlying liver disease and FibroScan probe were not significant. Although reliability varied according to operator (P<0.0005), operator experience was not significant. In a subanalysis including 434 patients with body mass index data, obesity influenced the rate of poorly reliable results (OR 2.93 [95% CI 0.95 to 9.05]; P=0.06). CONCLUSIONS: FibroScan failure and poorly reliable LSM are uncommon. The most important determinants of poorly reliable results are older age, obesity, higher liver stiffness and the operator, the latter emphasizing the need for adequate training.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".