Rapid fecal calprotectin testing to assess for endoscopic disease activity in inflammatory bowel disease: A diagnostic cohort study
Bibliographic record
Abstract
BACKGROUND AND AIM: With increasing numbers of patients diagnosed with inflammatory bowel disease (IBD), it is important to identify noninvasive methods of detecting disease activity. The aim of this study is to examine the diagnostic accuracy of fecal rapid calprotectin (FC) testing in the detection of endoscopically active IBD. PATIENTS AND METHODS: All consecutive patients presenting to outpatient clinics with lower gastrointestinal symptoms were prospectively recruited. Patients provided FC samples. Sensitivity (Sn), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) for FC were calculated. Receiver-operator characteristics (ROC) curve was used to identify the ideal FC cutoff that predicts endoscopic disease activity. Correlation between FC and endoscopic disease activity, disease location, and C-reactive protein (CRP) levels were measured. RESULTS: One hundred and twenty-six patients, of whom 52% were females, were included in the final analysis with a mean age of 44.4 ± 16.7 years. Comparing FC to endoscopic findings, the following results were calculated: A cutoff point of 100 μg/g showed Sn = 83%, Sp = 67%, PPV = 65%, and NPV = 85%; and 200 μg/g showed Sn = 66%, Sp = 82%, PPV = 73%, and NPV = 77%. Based on ROC curve, the best FC cutoff point to predict endoscopic disease activity was 140 μg/g. Using this reference, FC levels strongly correlated with colorectal, ileocolonic, and ileal disease and predicted endoscopic activity. CONCLUSIONS: FC is an accurate test when used as an initial screening tool for patients suspected of having active IBD. Given its noninvasive nature, it may prove to reduce the need for colonoscopy and be an added tool in the management of IBD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".