Validation of Dried Blood Spot Immunoreactive Trypsinogen as a Biomarker of Exocrine Pancreas Function in Children With Pancreas Disease
Bibliographic record
Abstract
INTRODUCTION: Radioimmunoassay (RIA)-serum trypsin serves as a pancreas function biomarker because its blood concentration correlates with duodenal trypsin output. With the unavailability of RIA tests and global availability of dried blood spot immunoreactive trypsin (DBS-IRT) testing as part of cystic fibrosis newborn-screening programs, IRT can be made accessible for routine clinical use. We aimed to validate DBS-IRT for its use as a biomarker of exocrine pancreas function in children older than the newborn age group. METHODS: Analytical accuracy of DBS-IRT was evaluated in 28 children (mean [SD] age: 13 [4.5] years) with known pancreas function status and simultaneous RIA-based serum trypsin testing. Reference ranges were established based on 134 metabolically stable children. Clinical validation was performed in 164 children with well-established pancreas phenotypes. Available cross-sectional imaging was reviewed to assess for pancreas atrophy to measure acinar cell loss. RESULTS: Total imprecision of DBS-IRT ranged from 7.1% to 16.4%; samples were stable samples at -20C for 28 days and showed excellent correlation to RIA-serum trypsin (r = 0.95, P < 0.001). A reference range of 6.9-21.2 ng/mL was newly established. DBS-IRT replicated serum trypsin in discriminating exocrine pancreatic insufficiency (EPI) in the cystic fibrosis group with high sensitivity (90.7%; 95% CI: 79.7%-96.9%) and specificity (94.7%; 95% CI: 73.9%-99.8%). Children with pancreatitis and EPI had significantly lower median DBS-IRT level compared with those without EPI ( P = 0.01). Low DBS-IRT was associated with pancreas atrophy ( P = 0.001). DISCUSSION: DBS-IRT was successfully validated as a biomarker of exocrine pancreas function which allows its use in clinical setting, in addition to fecal elastase to determine pancreas function.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".