Association Between Transient Elastography and Controlled Attenuated Parameter and Liver Ultrasound in Children With Cystic Fibrosis
Bibliographic record
Abstract
Methods to identify children with cystic fibrosis (CF) at risk for development of advanced liver disease are lacking. We aim to determine the association between liver stiffness measurement (LSM) by vibration‐controlled transient elastography (VCTE) with research ultrasound (US) patterns and conventional hepatic markers as a potential means to follow liver disease progression in children with CF. ELASTIC (Longitudinal Assessment of Transient Elastography in CF) is a nested cohort of 141 patients, ages 7‐21, enrolled in the Prediction by US of Risk of Hepatic Cirrhosis in CF (PUSH) Study. We studied the association between LSM with research‐grade US patterns (normal [NL], heterogeneous [HTG], homogeneous [HMG], or nodular [NOD]) and conventional hepatic markers. In a subgroup (n = 79), the association between controlled attenuation parameter (CAP) and US pattern was explored. Among 133 subjects undergoing VCTE, NOD participants (n = 26) had a significantly higher median (interquartile range) LSM of 9.1 kPa (6.3, 15.8) versus NL (n = 72, 5.1 kPa [4.2, 7.0]; P < 0.0001), HMG (n = 17, 5.9 kPa [5.2, 7.8]; P = 0.0013), and HTG (n = 18, 6.1 kPa [4.7, 7.0]; P = 0.0008) participants. HMG participants (n = 14) had a significantly higher mean CAP (SD) (270.5 dB/m [61.1]) compared with NL (n = 40, 218.8 dB/m [46.5]; P = 0.0027), HTG (n = 10, 218.1 dB/m [60.7]; P = 0.044), and NOD (n = 15, 222.7 dB/m [56.4]; P = 0.041) participants. LSM had a negative correlation with platelet count (rs = 0.28, P = 0.0071) and positive correlation with aspartate aminotransferase–to‐platelet ratio index (rs = 0.38, P = 0.0002), Fibrosis‐4 index (rs = 0.36, P = 0.0007), gamma‐glutamyltransferase (GGT; rs = 0.35, P = 0.0017), GGT‐to‐platelet ratio (rs = 0.35, P = 0.003), and US spleen size z‐score (rs = 0.27, P = 0.0073). Conclusion: VCTE is associated with US patterns and conventional markers in patients with liver disease with CF.
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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 | 0.001 | 0.005 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".