Inflammatory bowel disease therapies and demyelinating diseases: a practical guide to therapeutic benefit and risk
Bibliographic record
Abstract
Demyelinating diseases, particularly multiple sclerosis (MS), present a unique therapeutic challenge in the management of inflammatory bowel disease (IBD). Although rare, the co-occurrence of IBD and demyelinating disorders is well-documented and may reflect shared immune, genetic, and environmental risk factors. As the therapeutic landscape of IBD expands to include biologics and small molecules that target immune pathways also implicated in MS, concerns around neurological safety have grown. In particular, anti-tumor necrosis factor agents have been consistently linked to new-onset or worsening demyelinating events, while other treatments such as sphingosine-1-phosphate receptor modulators and natali-zumab are licensed for both IBD and MS, though real-world data in patients with coexisting disease remain limited. This review synthesizes current evidence regarding the neurological safety and efficacy of IBD therapies in the context of demyelinating disease. It proposes a practical framework for clinicians, addressing management strategies for patients with confirmed MS, those at increased risk, and individuals who develop neurological symptoms during treatment. In the absence of formal guidelines, multidisciplinary collaboration, early recognition of symptoms, and careful treatment selection are important to optimize both gastrointestinal and neurological outcomes.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".