Incidence Rates of Psoriasis in Children With Inflammatory Bowel Disease and Juvenile Arthritis Treated With Tumor Necrosis Factor Inhibitors and Disease-Modifying Antirheumatic Drugs
Bibliographic record
Abstract
Objective To estimate the differential effect of tumor necrosis factor inhibitor (TNFi) therapies and presence or absence of conventional synthetic disease-modifying antirheumatic drugs (DMARDs) on the incidence of psoriasis (PsO) in children with inflammatory bowel disease (IBD), juvenile idiopathic arthritis (JIA), and chronic nonbacterial osteomyelitis (CNO). Methods This was a retrospective cohort study from 2008 to 2020. TNFi and DMARD exposures were dichotomized as ever/never. The primary outcome was incident PsO. Incidence rates (IRs) of PsO were stratified by underlying diagnosis, TNFi agent, and DMARD use. Poisson regression was used to assess the IR ratios (IRRs) between exposure groups. Results There were 5088 children who met the inclusion criteria: 3794 (75%) had IBD, 1189 (23%) had JIA, and 105 (2%) had CNO. Of the 2023 children with TNFi exposure, 613 (30%) and 1410 (70%) were with or without a DMARD, respectively. When controlling for DMARD, sex, and family history of PsO, the IRR of developing PsO in patients exposed to adalimumab (ADA) was 2.70 times higher (95% CI 1.53-4.75; P < 0.001) than those who did not receive any TNFi treatment. IRR was lower, but not significantly different, for patients exposed to infliximab (IFX; IRR 2.34, 95% CI 1.56-3.51; P < 0.001) and etanercept (ETN; IRR 2.21; 95% CI 1.17-4.21; P = 0.006) compared to TNFi-unexposed patients. IRR of TNFi exposure was lower by 0.25 (P < 0.001) in DMARD-exposed patients compared to non–DMARD-exposed patients. Conclusion IRR of TNFi-induced PsO was not significantly different among ADA, IFX, and ETN. However, for patients with exposure to any of the TNFi evaluated, the IRR was significantly lower in those also exposed to a DMARD.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".