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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".