Feasibility and reliability of the FibroScan S2 (pediatric) probe compared with the M probe for liver stiffness measurement in small adults with chronic liver disease
Bibliographic record
Abstract
BACKGROUND: The success of liver stiffness measurement (LSM) by transient elastography (TE, FibroScan) is influenced by anthropometric factors. In smaller adults, the M probe may fail due to narrow intercostals spaces and rib interference. We aimed to compare LSM using the FibroScan S2 (pediatric) probe with the M probe in small adults with chronic liver disease. MATERIAL AND METHODS: In this prospective study, 41 liver disease patients and 18 controls with a thoracic perimeter ≤ 75 cm underwent LSM using the FibroScan M and S2 probes. TE failure was defined as no valid LSMs and unreliable examinations as < 10 valid LSMs, an interquartile range (IQR)/LSM > 30%, or success rate < 60%. RESULTS: TE failure was not observed and reliability did not differ between the M and S2 probes (86% vs. 95%; P = 0.20). Liver stiffness measured using the M and S2 probes was highly correlated (ρ = 0.81; P < 0.0005) and median liver stiffness did not differ between probes (4.5 vs. 4.4 kPa; P = 0.10). However, in participants with a skin-capsular distance ≥ 15 mm, median liver stiffness was higher using the S2 probe (5.5 vs. 4.9 kPa; P = 0.008). When compared with validated liver stiffness cut-offs, the S2 probe would have overestimated the stage of fibrosis compared with the M probe in 10% of patients. CONCLUSIONS: The FibroScan S2 probe does not improve the feasibility of LSM in adults of smaller stature and may overestimate liver stiffness compared with the M probe. The FibroScan M probe should remain the preferred tool for LSM in small adults with chronic liver disease.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".