P651 Disease severity and intensity of therapy predicts serious adverse events in paediatric ulcerative colitis: the DEVELOP experience
Bibliographic record
Abstract
DEVELOP is a multi-centre (USA, Canada, European Union), prospective, observational registry of the long-term safety and clinical status of 6070 paediatric patients with inflammatory bowel disease (IBD including 1678 ulcerative colitis [UC] patients) treated with anti-tumour necrosis factor biologics (aTNF) and/or other medical therapies as part of physician dictated clinical care. AIM: To identify covariates that were significant predictors to time to first serious adverse event (SAE) in patients with UC. A SAE is defined as any undesirable experience that results in hospitalisation, requires medical intervention or is otherwise life-threatening. Physicians participating in the registry prescribe IBD treatments based on their usual clinical practice and standards of care. Patients are categorised into cohorts according to their IBD medication exposure. The cohorts represent prevalent or incident exposure, including patients receiving therapy prior to enrolment and patients receiving therapy during registry follow-up. Enrolment was targeted such that about 50% of the initial population had been exposed to originator infliximab. Hazard ratio (HR) for SAE was calculated by stepwise Cox regression modelling. Time to first SAE are shown in Table 1. This includes 1121 UC patients who were exposed to aTNFs as the only biologic and/or non-biologics and had at least 1 post-baseline follow-up visit, complete baseline covariate data, and complete disease severity data (partial Mayo score) at event or censoring. The covariates that were significantly associated with a shorter duration of time to first SAE in UC patients included combination therapy with aTNF/immunomodulators (IMM) and corticosteroids (CS) or combination therapy with aTNF and CS. Monotherapy with CS, disease activity (hazard ratio [HR] 2.657) and recent hospitalisation were also significantly associated. Conversely, combination therapy with aTF and IMM or monotherapy with aTNF or IMM alone were not associated with significantly increased risk of time to first SAE. In terms of HR, disease severity was the strongest predictor of time to first SAE. Combination therapy with aTNF and CS and also triple therapy with aTNF, CS and IMM were predictors as was monotherapy with CS. On the other hand, monotherapy with aTNF or with IMM were not found to be significant predictors.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".