Bibliographic record
Abstract
New evidence points to a possible association of multiple sclerosis (MS) with IBD. Tumour necrosis factor (TNF) is involved in the pathogenesis of MS. Paradoxically, administration of anti-TNF monoclonal antibodies to IBD patients has led to exacerbation of MS or triggered demyelination. TNF receptor 1 (TNFR1) mediates demyelination and TNFR2 mediates remyelination, suggesting that a more selective approach to TNF antagonism may be required for an anti-TNF strategy to be effective in MS. Conversely, interferon treatment for MS may worsen IBD. Anti-lymphocyte trafficking strategies such as alpha4 integrin blockers are effective in both these diseases. Recent advances in small molecule development in this area may provide further effective therapies. Common inflammatory cytokine and signaling pathways may be shared between MS and IBD, such as TNF, interleukin (IL)-12/23, IL-17, CD40 and STAT3. However, in contrast to Crohn's disease, ustekinumab has not shown efficacy in MS. Vitamin D deficiency is a hot topic in both diseases. Several drugs developed for MS, e.g. glatiramer acetate, are also being studied in IBDs. As in rheumatoid arthritis, MS also serves as an example of a chronic relapsing inflammatory disease where disease modification is the goal of treatment based on objective evidence derived from imaging, in turn providing examples of how to conduct IBD studies in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".