Validation of a point-of-care desk top device to quantitate fecal calprotectin and distinguish inflammatory bowel disease from irritable bowel syndrome
Bibliographic record
Abstract
BACKGROUND AND AIMS: The neutrophil protein calprotectin has been investigated as a surrogate marker for intestinal inflammation. This study was designed to contrast fecal calprotectin levels in patients with inflammatory and non-inflammatory intestinal diseases and to compare the results obtained from the standard ELISA-based method with those obtained from a novel desk-top device. METHODS: Soluble proteins were extracted from stool samples of 50 participating patients, including those diagnosed with Ulcerative Colitis, Crohn's Disease or IBS, and volunteers with no known intestinal problems. Calprotectin was assessed in the extracted material using the "desk top" Bühlmann Quantum Blue Reader® or by standard ELISA techniques. RESULTS: The mean concentration of calprotectin in the IBD patients group was significantly higher than the mean concentration found in IBS patients and healthy controls (p=0.01). Calprotectin concentrations in IBS patients and controls were indistinguishable. IBD patients that had undergone recent surgery displayed scores similar to controls and IBS patients. Excluding these patients yielded a specificity of 100% for results from both CD and UC patients and an accuracy rate of 1 for CD and 0.89 for UC patients in ROC analysis. Quantum Blue Reader® calprotectin levels were available within 30 min and correlated well with results derived from standard ELISA assays, which took over 8h to complete. CONCLUSION: Our results confirm the effective use of fecal calprotectin levels in differentiating non-inflammatory from active inflammatory intestinal diseases. The desk top Bühlmann Quantum Blue Reader® exhibits a fast, non-invasive, and reliable way of identifying an inflammatory intestinal 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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".