Transient Elastography in the Evaluation of Cystic Fibrosis–Associated Liver Disease: Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Complications of cystic fibrosis-associated liver disease (CFLD) are a leading nonpulmonary cause of death. Transient elastography (TE) has recently been investigated to detect CFLD. This study reviews the current literature for TE in the detection CFLD. A meta-analysis was performed to determine the ideal liver stiffness measurement (LSM) cutoff. METHODS: PubMed, Medline, EMBASE and Web of Science were searched from inception until April 2016 for publications involving the detection of CFLD with TE. Data were extracted using a fixed protocol (a priori design) including study design, population characteristics, probe size and AST Platelet Ratio Index (APRI). RESULTS: Diagnostic properties were summarized from six studies of 605 patients. Cutoff for LSM was determined using pooled data submitted by authors. The cutoff for LSM and APRI were ≥5.95 kPa and ≥0.329 respectively, yielding a sensitivity, specificity and area under receiver operator characteristic of 55%, 87%, 0.76, 52%, 93% and 0.84 for LSM and APRI, respectively. When LSM ≥5.95 kPa and APRI ≥0.329, the sensitivity, specificity, positive predictive value and negative predictive value were 43%, 99%, 92% and 87% with a diagnostic odds ratio of 74.9. A bivariate metaregression model showed that pediatric specific cutoffs for liver stiffness and APRI may not be necessary. CONCLUSION: Individually, LSM and APRI have poor sensitivity but good specificity for detecting CFLD. They are most useful when combined. We propose that patients with LSM ≥5.95 kPa and APRI ≥0.329 be investigated thoroughly for the presence of cystic fibrosis-associated 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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".