Diagnostic accuracy of serum matrix metalloproteinase-7 as a biomarker of biliary atresia in a large North American cohort
Bibliographic record
Abstract
BACKGROUND AND AIMS: High levels of serum matrix metalloproteinase-7 (MMP-7) have been linked to biliary atresia (BA), with wide variation in concentration cutoffs. We investigated the accuracy of serum MMP-7 as a diagnostic biomarker in a large North American cohort. APPROACH AND RESULTS: MMP-7 was measured in serum samples of 399 infants with cholestasis in the Prospective Database of Infants with Cholestasis study of the Childhood Liver Disease Research Network, 201 infants with BA and 198 with non-BA cholestasis (age median: 64 and 59 days, p = 0.94). MMP-7 was assayed on antibody-bead fluorescence (single-plex) and time resolved fluorescence energy transfer assays. The discriminative performance of MMP-7 was compared with other clinical markers. On the single-plex assay, MMP-7 generated an AUROC of 0.90 (CI: 0.87-0.94). At cutoff 52.8 ng/mL, it produced sensitivity = 94.03%, specificity = 77.78%, positive predictive value = 64.46%, and negative predictive value = 96.82% for BA. AUROC for gamma-glutamyl transferase = 0.81 (CI: 0.77-0.86), stool color = 0.68 (CI: 0.63-0.73), and pathology = 0.84 (CI: 0.76-0.91). Logistic regression models of MMP-7 with other clinical variables individually or combined showed an increase for MMP-7+gamma-glutamyl transferase AUROC to 0.91 (CI: 0.88-0.95). Serum concentrations produced by time resolved fluorescence energy transfer differed from single-plex, with an optimal cutoff of 18.2 ng/mL. Results were consistent within each assay technology and generated similar AUROCs. CONCLUSIONS: Serum MMP-7 has high discriminative properties to differentiate BA from other forms of neonatal cholestasis. MMP-7 cutoff values vary according to assay technology. Using MMP-7 in the evaluation of infants with cholestasis may simplify diagnostic algorithms and shorten the time to hepatoportoenterostomy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".