Clinicians’ Guide to the Use of Fecal Calprotectin to Identify and Monitor Disease Activity in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND: Objective monitoring of the severity of inflammation in patients with inflammatory bowel disease (IBD) is an essential part of disease management. However, repeat endoscopy to define extent and severity of inflammation is not practical. Fecal calprotectin (FC) is a biomarker that can be used as a surrogate test to distinguish inflammatory from noninflammatory gastrointestinal disease. METHODS: A targeted search of the literature regarding FC, focusing primarily on the past three years, was conducted to develop practical clinical guidance on the current utility of FC in the routine management of IBD patients. RESULTS: It is recommended that samples for FC testing be obtained from the first bowel excretion of the day. FC testing should be used as standard of care to accurately confirm inflammation and 'real-time' disease activity when a clinician suspects an IBD flare. Although FC is a reliable marker of inflammation, its role in routine monitoring in improving long-term outcomes has not yet been fully assessed. Based on available evidence, the authors suggest the following cut-off values and management strategies: when FC levels are <50 µg⁄g to 100 µg⁄g, quiescent disease is likely and therapy should be continued; when FC levels are >100 µg⁄g to 250 µg⁄g, inflammation is possible and further testing (eg, colonoscopy) is required to confirm inflammation; and when FC levels are >250 µg⁄g, active inflammation is likely and strategies to control inflammation should be initiated (eg, optimizing current therapies or switching to an alternative therapy). DISCUSSION: FC is a useful biomarker to accurately assess the degree of inflammation and should be incorporated into the management of patients with IBD.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 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".