Using machine learning to predict clinical remission with exclusive enteral nutrition in pediatric Crohn disease
Bibliographic record
Abstract
Abstract Objectives Exclusive enteral nutrition (EEN) is a first‐line treatment for induction of remission in luminal pediatric Crohn disease (pCD). However, as the efficacy of EEN varies from patient to patient, there is a need to distinguish between responders and nonresponding patients. This study had two aims. First, to develop a model to predict EEN‐induced clinical remission (weighted pediatric CD activity index [wPCDA] ≤ 12.5) using baseline clinical information. Second, to develop a model to predict corticosteroid‐free sustained clinical remission post‐EEN induction (wPCDA ≤ 12.5, for ≥36 weeks after EEN). Methods We applied machine learning to clinical and laboratory data from a prospectively followed cohort of pCD patients who received EEN as their first treatment for CD ( n = 308). This learning algorithm used feature selection and k‐fold (internal) cross‐validation to systematically find the model with the best combination of features and hyperparameter settings. To estimate the quality of the learned model, we used k‐fold (external) cross‐validation. Results Clinical, laboratory, and treatment data were compiled into two different datasets: EEN clinical remission at the end of EEN treatment (mean of 60 days; n = 114) and corticosteroid‐free sustained clinical remission post‐EEN induction ( n = 206). Our resulting models were effective, with external area under the curves of 0.65 0.015 and 0.60 0.018. Moreover, a permutation label test showed that our learning process was stable and significantly different from chance, at p ‐values of 0.002 and 0.01, respectively. Conclusion Our models, based on accessible clinical features, were able to effectively predict EEN success above chance. This supports the plausibility of building clinical tools to assist precision therapy for pCD patients.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".