Within-Stool and Within-Day Sample Variability of Fecal Calprotectin in Patients With Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND AND GOALS: The use of fecal calprotectin (FC) as a stool biomarker for differentiating inflammatory bowel disease (IBD) from IBS has been well validated, and there is a strong correlation between FC and the presence of endoscopic inflammatory lesions. However, recent studies have demonstrated intraindividual sample variability in patients with IBD, possibly limiting the reliability of using a single sample for monitoring disease activity. Our aim was to assess the within-stool and within-day sample variability of FC concentrations in patients with IBD. STUDY: We examined a cross-sectional cohort of 50 adult IBD patients. Eligible patients were instructed to collect 3 samples from different parts of the stool from their first bowel movement of the day and 3 samples from each of up to 2 additional bowel movements within 24 hours. FC concentrations were measured by a rapid, quantitative point-of-care test using lateral flow technology (Quantum Blue). Descriptive statistics were used to assess FC variability within a single bowel movement and between different movements at different FC positivity cutoffs. RESULTS: Within a single bowel movement, there was clinically significant sample variability ranging from 8% to 23% depending on the time of the day or on the FC positivity cutoff value. Between bowel movements, there was clinically significant sample variability ranging from 13% to 26% depending on the FC positivity cutoff. CONCLUSIONS: Considering a single FC sample, the first sample of the day with an FC positivity cutoff of 250 μg/g provided the most reliable indication of disease activity.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".