Predicting endoscopic remission in Crohn’s disease by the modified multiplier SES-CD (MM-SES-CD)
Bibliographic record
Abstract
BACKGROUND AND AIMS: The Simple Endoscopic Score for Crohn's disease (SES-CD) is the primary tool for measurement of mucosal inflammation in clinical trials but lacks prognostic potential. We set to develop and validate a modified multiplier of the SES-CD (MM-SES-CD), which takes into consideration each individual parameter's prognostic value for achieving endoscopic remission (ER) while on active therapy. METHODS: In this posthoc analysis of three CD clinical trial programmes (n=350 patients, baseline SES-CD ≥ 3 with confirmed ulceration), data were pooled and randomly split into a 70% training and 30% testing cohort. The MM-SES-CD was designed using weights for individual parameters as determined by logistic regression modelling, with 1-year ER (SES-CD < 3) being the dependent variable. A cut point score for low and high probability of ER was determined by using the maximum Youden Index and validated in the testing cohort. RESULTS: Baseline ulcer size, extent of ulceration and presence of non-passable strictures had the strongest association with 1-year ER as compared with affected surface area, with differential weighting of individual parameters across disease segments being observed during logistic regression. The MM-SES-CD was generated using this weighted regression model and demonstrated strong discrimination for ER in the training dataset (area under the receiver operator curve (AUC) 0.83, 95% CI 0.78 to 0.94) and in the testing dataset (AUC 0.82, 95% CI 0.77 to 0.92). In comparison to the MM-SES-CD scoring model, the original SES-CD score lacks accuracy (AUC 0.60, 95% CI 0.55 to 0.65) for predicting the achievement of ER. CONCLUSIONS: We developed and internally validated the MM-SES-CD as an endoscopic severity assessment tool to predict one-year ER in patients with CD on active therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".