P186 Faecal calprotectin (FCal): a valuable non-invasive tool in the management of IBD
Bibliographic record
Abstract
FCal emerged as useful tool for IBD management, but varied assay methods, cut-offs, scenarios, phenotypes and populations may influence usefulness. Two substudies were designed: (1) To investigate the value of FCal in mucosal healing (MH) prediction (optimal cut-off, specificity, sensitivity, PPV, NPV) and thresholds for clinical activity and phenotypes and (2) to evaluate the ability of FCal monitoring in IBD in remission to predict relapse. FCal was determined with Bühlmann® ELISA in IBD patients. from a Latin-American centre. Substudy-1 (MH prediction and activity/pattern of IBD): Included 100 IBD patients: (44 UC 56 CD), who underwent routine colonoscopy (VCC) with categorisation by IBSEN score (Frøslie KF, 2007) ‘MH’(scores 0–1) and ‘non-MH’, colleting FCal samples within previous week. Optimal FCal cut-off for ‘MH’prediction (opt-MH cut-off) was calculated (ROC analysis). Substudy-2 (Prediction of relapse): included 50 UC and 50 CD in clinical remission (≥3 months), FCal: basal, ≥biannual, VCC basal/final. Analysis: Kaplan–Meier survival analysis for FCal levels above and below opt-MH cut-off. Mean follow-up 23.0 ± 11.8 months. Global definitions of clinical activity: P.Mayo (UC), HBI (CD), Location/Extent (Montreal). Substudy-1: FCal levels (Mean ± SD) in patients. with ‘MH’ were significant lower vs. ‘Non-MH’: UC (191.3 ± 174.6 vs. 621.1 ± 368.3, p = 0.0001) and CD (237.0 ± 196.9 vs. 618.5 ± 319.3, p < 0.0001) Kruskal–Wallis. Opt-MH cut-off was 242 μg/g, AUC 0.84 (95% CI 0.753–0.906) p = 0.0001, sensitivity: 76.4%, specificity: 84.5%, PPV: 85.7%, NPV: 74.5%. By clinical criteria FCal was lower (p < 0.0001) in remission vs. activity in UC (165.7 ± 14.1 vs. 630.3 ± 349.6) and CD (276.4 ± 250.1 vs. 662.1 ± 289.9), but the cut-off was higher (284 μg/g) than opt-MH cut-off. In endoscopically active CD patients, FCal levels were higher in colonic CD (851.9 ± 232.0) vs. other locations 544.4 ± 313.3 (p = 0.04). Substudy-2: Cumulative probabilities of clinical relapse at 6, 12, 18, 24 months of patients with Fcal ≥ 242 μg/g (n = 34) were 20.6%, 38.2%, 44.7%, 51.6%, and rates with FCal under cut-off (n = 66) were 1.5%, 3.1%, 5.1% and 7.9%, respectively, HR: 14.22 (95% CI 6.18–32.72), p < 0.0001, sensitivity: 85%, specificity 82.7%, PPV: 67.7%, NPV: 93.9%. Globally, relapsed 15 (30%) of UC and 12 (24%) of CD (NS). Clinical relapses with Fcal ≥ 242 were 67.7% vs. 6.1% under cut-off, p < 0.0000001, endoscopic relapses (available in 91 patients) with FCal ≥ 242: 75% (1) Fcal was a good predictor of MH in UC and CD according opt-MH cut-off (242 μg/g), (2) FCal values were significantly lower in remission vs. activity, in UC and CD, but in endoscopically active colonic CD, FCal was higher vs. other locations, (3) FCal showed to be an effective tool to predict relapse for levels above opt-MH cut-off.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".