A79 CHARACTERISTICS OF PEDIATRIC INFLAMMATORY BOWEL DISEASE AT DIAGNOSIS RELATED TO EARLIER USE OF BIOLOGIC THERAPY
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is usually treated with increasingly potent anti-inflammatory and immunomodulator medications. One of the most efficacious medication classes currently available are biologic agents. Initiation of biologic therapy is often delayed until other therapies are tried. Often, features suggestive of severe disease course are used as justification for starting biologics. However, it is unclear if disease severity is truly connected to biologic response. Clinical, biochemical, and radiographic characteristics can identify pediatric patients with IBD at diagnosis who receive biologic therapy earlier than those without these characteristics. Charts of all pediatric patients with IBD followed at our centre were reviewed. Kaplan-Meier curves were evaluated comparing characteristics at diagnosis with time to initiation of biologics. Data from 198 patients followed at our centre between 2001 and 2015 was analyzed, 57.6% of patients had Crohn’s disease (CD), 27.8% had ulcerative colitis (UC), and 14.6% had IBD-unclassified. The mean follow up time was 47.8 months, and ranged from 9 to 181 months. Overall, 55.5% of the patients received biologics, the mean time to biologic initiation was 21.5 months. Characteristics at diagnosis that were associated with earlier initiation of biologics based on Kaplan-Meier curves for both CD and UC were: older age, higher disease activity index and lower serum albumin level. In our cohort, older patients with more severely active disease and lower serum albumin levels at the time of IBD diagnosis were more likely to initiate biologic therapy earlier than those without these characteristics. Identification of these characteristics may help inform decisions to initiate biologics earlier in the IBD disease course. Prospective studies are needed to better understand how to identify patients with IBD who would benefit from starting biologic therapy at diagnosis. None
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".