A70 SEVERE ENTERITIS AND IGA VASCULITIS IN A PEDIATRIC PATIENT WITH ULCERATIVE COLITIS ON VEDOLIZUMAB
Bibliographic record
Abstract
Abstract Background Pediatric Ulcerative Colitis (UC) is often unsatisfactorily controlled using ‘conventional’ therapies, thus there has been increasing use of biologic therapies and small molecules to try and improve rates of clinical remission and mucosal healing. Consequently, pediatric gastroenterologists and generalists should be aware of the complications and side effects of these newer molecules. Vedolizumab (VDZ) is a fully humanised anti-integrin therapy that is thought to limit systemic side effects given its gut-specific mechanism. Aims Here we report the unique case of a 14-year-old male with UC on vedolizumab presenting with severe abdominal pain, GI bleeding, markedly elevated inflammatory markers and subsequent purpura. Methods N/A Results A diagnosis of IgA vasculitis (IgAV) was made after extensive imaging, endoscopy and subspecialty consultation. Clinical course was complicated but ultimately responded to high dose steroid treatment without cessation of VDZ. Conclusions This case illustrates a potentially independent pathway behind IgAV in a patient on a gut-selective therapy with UC. This is the first pediatric case of vasculitis in a patient with UC on VDZ. Funding Agencies None
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".