The Relationship Between the Endoscopic Healing Index, Fecal Calprotectin, and Magnetic Resonance Enterography in Crohn’s Disease
Bibliographic record
Abstract
INTRODUCTION: The serum-based endoscopic healing index (EHI) test identifies endoscopic Crohn's disease (CD) activity. Data are lacking on the relationship between EHI with other endpoints. We assessed the relationship between EHI and the simplified Magnetic Resonance Index of Activity. MATERIALS AND METHODS: Data were prospectively collected on patients with CD with either an EHI or fecal calprotectin (FCAL) within 90 days of magnetic resonance enterography (MRE). Diagnostic accuracy was assessed using area under the receiver operator characteristics. Proportions with any, severe, and terminal ileum MR inflammation were compared above/below identified thresholds for both EHI and FCAL. RESULTS: A total of 241 MREs paired to either EHI or FCAL from 155 patients were included. Both EHI and FCAL had similar accuracy to diagnose inflammation (area under the receiver operator characteristics: EHI: 0.635 to 0.651, FCAL: 0.680 to 0.708). Optimal EHI values were 42 and 26 for inflammation on MRE and endoscopy, respectively. Patients with EHI ≥42 (100% vs. 63%, P =0.002), FCAL >50 µg/g (87% vs. 64%, P <0.001) and FCAL >250 µg/g (90% vs. 75%, P =0.02) had higher rates of simplified Magnetic Resonance Index of Activity ≥1 compared with lower values. EHI differentiated ileitis numerically more than FCAL (delta: 24% to 25% vs. 11% to 21%). Patients with FCAL ≥50 µg/g had higher rates of severe inflammation compared with FCAL <50 µg/g (75% vs. 47%, P <0.001), whereas smaller differentiation existed for EHI threshold of 42 (63% vs. 49%, P =0.35). CONCLUSION: Both EHI and FCAL were specific in their confirmation of inflammation and disease activity on MRE in patients with CD. However, MRE-detected inflammation was frequently present in the presence of low EHI and FCAL in similar proportions.
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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 | 0.002 | 0.011 |
| 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 | 0.000 | 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".