Role of transient elastography and APRI in the assessment of pediatric cystic fibrosis liver disease
Bibliographic record
Abstract
Background: Diagnosis and monitoring of cystic fibrosis liver disease (CFLD) is challenging. Transient elastography (TE) is a rapid, non-invasive method for assessing liver fibrosis. Its role in detecting fibrosis in CFLD has only begun to be explored. The aspartate aminotransferase to platelet ratio index (APRI) has been validated as a predictor of hepatic fibrosis in other chronic liver diseases. The purpose of this study was to assess the utility of APRI and TE in identifying liver fibrosis in pediatric CF patients. Methods: Patients aged 2–18 years were recruited from the British Columbia Children’s Hospital CF clinic. Patients were determined to have CFLD using standard criteria. Charts were reviewed, and each patient underwent TE. Results: Of the 55 patients included in the study (50.9% male, mean age 11.6 y), 22 (40%) had CFLD. All mean liver enzymes were higher in the CFLD group, notably alanine transaminase ( p = 0.031). Mean liver stiffness (LS) and APRI were also higher in the CFLD group (LS: 5.9 versus 4.5 kPa, p = 0.015; APRI: 0.40 versus 0.32, p = 0.119). Linear regression showed a mild positive association between the two ( r2 = 0.386). Conclusions: TE values were higher among CFLD patients and correlated with APRI values, suggesting that these tools may have clinical applications for identifying and following this population. Further research is needed on a larger scale to determine the relative value and clinical utility of TE and APRI among patients with CFLD.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".