N023 Characterisation of IBD patients with multiple sclerosis at a large IBD unit
Bibliographic record
Abstract
Targeting TNF-α is an important and effective therapeutic approach for patients with moderate-to-severe inflammatory bowel diseases (IBD). TNF-α antibodies have a favourable safety profile. However, known side effects of this treatment are for example allergic reactions and infections. Rare adverse events like central nervous system (CNS) demyelinating disorders (e.g. multiple sclerosis, optic neuritis) in association with TNF-α antibody (AB) therapy have been reported. In summary of this complex and important therapeutic area, we reviewed our patient collective of 1200 patients for known CNS diagnosis, e.g. multiple sclerosis (MS) independent of their IBD treatment. All patients were recruited from our IBD outpatient clinic seen between June 2006 and October 2017. Only patient with MS and IBD diagnosis were included. The data were retrospectively collected by chart review and patients were split into 2 groups (CNS diagnosis without any TNF-alpha antibody treatment= cohort 1; and CNS diagnosis developed during TNF-alpha AB treatment=cohort 2). Data were analysed for age, age at diagnosis, sex, IBD diagnosis, Montreal Classification (Behaviour (B), Location (L)) and IBD family history with Student t-test and chi-square test. Overall, 13 patients with CNS diagnosis were included in this study. Six IBD patients were diagnosed with MS (cohort 1) without TNF-alpha AB treatment. Seven IBD patients were diagnosed during TNF-alpha AB treatment with MS (cohort 2). IBD diagnosis in cohort 1 was Crohns disease (CD) in 83.3 %, Ulcerative colitis (UC) in 16.7 %; and cohort 2 was CD in 57.1 % and UC in 42.9 %. Patients of cohort 2 were diagnosed significant early with IBD (p = 0.01; 16–53 years; SD 9.7 years) compared with cohort 1 (34–53 years; SD 7.3 years). The time between first Anti-TNF AB application and MS diagnosis differs about 12–109 months in cohort 2. Patients of this cohort reported more extraintestinal manifestations like arthralgia (p = 0.16; 85.7% vs. 50.0%) before MS diagnosis. Disease Behaviour (Montreal Classification) of cohort 2 patients was more severe (p = 0.19; B3 60.0%, B1 20.0%, B1p 20.0%) compared with cohort 1 (B2 40.0 %, B1 60.0%). In cohort 1 more patient had a family history of IBD than in cohort 2 (p = 0.20; 60.0% vs. 20.0%). Despite the small sample size, in our cohort patients with MS developed during TNF-AB have a more severely IBD disease with a significant earlier onset of their symptoms including inflammation, structuring and penetrating complication and extraintestinal manifestations than MS patients without TNF-AB. In IBD patients unit neurological symptoms (like paresthesia, weakness at extremities) CNS disorders should be at one’s mind, especially in TNF-α AB-treated 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".