Development of Three Practical Indices for Mucosal Healing Among Patients With Moderate to Severe Crohnʼs Disease
Bibliographic record
Abstract
Healing of the intestinal mucosa is considered a valid and important metric of improvement in Crohn's disease (CD). However, a patient must undergo an endoscopy to verify the presence of mucosal ulceration. Regular monitoring using endoscopy is challenging owing to its expensive and intrusive nature. There are currently no easily accessible proxy measures for identifying patients who are likely to have achieved mucosal healing (MH) in CD. The practical MH indices were developed using intent-to-treat (ITT) patient-level data from the EXTend the Safety and Efficacy of Adalimumab Through ENDoscopic Healing (EXTEND) trial (160 mg adalimumab at Week 0, 80 mg at Week 2, and randomized to 40 mg every other week or placebo at Week 4). Each index corresponds to 1 of 3 mucosal healing measures: Simple Endoscopic Score for CD (SES-CD) <5, CD Endoscopic Index of Severity (CDEIS) score ≤4.24, and observed MH based on a review committee opinion. For each measure, a logistic regression model was developed to predict MH at Week 12 using demographic, laboratory, and CD severity characteristics. Important predictors were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) procedure, which identifies a parsimonious model while retaining predictive accuracy. These 3 indices, named as SES-CD Based Index, CDEIS Based Index, and Observed MH Based Index, were evaluated using the accuracy (c-statistic), sensitivity, and specificity measures. Important factors associated with MH were similar across the 3 indices (Table). The performance of each index was also evaluated and listed in the table. Platelet count, CRP, and CDAI (number of liquid or very soft stools) are the common factors associated with MH across all 3 indices. The accuracy rates suggest the practical indices may be a clinically useful guide for identifying patients who are likely or unlikely to have achieved MH.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".