Clinical utility of serodiagnostic testing in suspected pediatric inflammatory bowel disease
Bibliographic record
Abstract
OBJECTIVES: Confronted with nonspecific symptoms, accurate screening tests would be useful to clinicians to distinguish between functional childhood disorders and inflammatory bowel disease (IBD), thus avoiding invasive diagnostic testing. Traditional ulcerative colitis-specific perinuclear antineutrophil cytoplasmic antibody (pANCA) and Crohn's disease-specific anti-Saccharomyces cerevisiae antibody (ASCA) serodiagnostic assays have recently been modified, with ELISA cut-off values recalculated to maximize sensitivity. The aim of this study was to determine whether the combination of these serodiagnostic tests could maximize diagnostic accuracy and minimize invasive investigations in pediatric patients presenting with nonspecific symptoms suggestive of IBD. METHODS: With investigators blinded to clinical diagnoses, ASCA, ANCA, and pANCA profiles were obtained prospectively from 128 patients undergoing complete diagnostic evaluation for IBD. In phase I, diagnostic accuracy and predictive values of the modified and traditional assays were compared for the IBD (n = 54) and non-IBD groups (n = 74). In phase II, the overall accuracy of a novel sequential diagnostic testing strategy was determined. Additionally, the potential number of invasive investigations avoided with the hypothetical application of this strategy to the cohort was determined. RESULTS: For phase I, the modified serodiagnostic assay was more sensitive (81 vs 69%), whereas the traditional assay had a higher specificity (96 vs 72%) for IBD (p < 0.05) For phase II, false-positive diagnoses would have been reduced by 81%, yielding an overall sequential testing strategy accuracy of 84%. CONCLUSIONS: The incorporation of sequential noninvasive testing into a diagnostic strategy may avoid unnecessary and costly evaluations and facilitate clinical decision making when the diagnosis of IBD in children is initially uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".