OP015 Biomarker correlation with endoscopic outcomes in patients with Crohn’s disease: data from CALM
Bibliographic record
Abstract
Management of Crohn’s disease (CD) is moving towards the therapeutic goal of mucosal healing using biomarkers of inflammation, faecal calprotectin (FC) and C-reactive protein (CRP), to optimise therapy. CALM demonstrated superior endoscopic outcomes in patients whose treatment was escalated based on a tight control algorithm using symptoms and biomarkers than in patients managed conventionally,1 but the optimal biomarker cut-offs to predict mucosal healing have not been established. In this analysis from CALM, association of endoscopic outcomes with FC and CRP cut-offs was investigated. Adult patients with CD (N = 244) were assessed for association of mucosal healing (CD Endoscopic Index of Severity [CDEIS] < 4) and no deep ulcers (primary endpoint in CALM) and endoscopic response (CDEIS decrease >5 from baseline [BL]) with levels of FC and CRP at 48 weeks using Chi-square test. Analysed cut-offs for FC (<250 or ≥250 µg/g) and CRP (<5 or ≥5 mg/l) at 48 weeks were based on criteria for treatment escalation in the tight control group. Data are summarised as observed in patients with both endoscopic outcome and biomarker level. Association between the two endoscopic endpoints and CRP and FC cut-offs at 48 weeks is shown in Table. Significantly greater proportions of patients with CRP concentrations of <5 mg/l achieved endoscopic outcomes in CALM. Similar findings, to a greater extent, were associated with FC <250 μg/g. Even higher proportion of patients achieved the two endoscopic endpoints when both CRP and FC were considered. Correlation of biomarker cut-offs with endoscopic outcomes is an important finding for future management of CD. Additional studies are needed to further define the biomarker cut-offs. 1. Colombel J-F, et al. Lancet, 2017.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".