Dosing Variation at Initiation of Adalimumab and Etanercept and Clinical Outcomes in Juvenile Idiopathic Arthritis: A Childhood Arthritis and Rheumatology Research Alliance Registry Study
Bibliographic record
Abstract
OBJECTIVE: To determine the dose-response relationship of tumor necrosis factor (TNF) inhibition in the treatment of juvenile idiopathic arthritis (JIA). METHODS: Participants of the Childhood Arthritis and Rheumatology Research Alliance Registry were eligible for inclusion in the analyses if they started TNF inhibition treatment for JIA. The primary treatment response was determined 3 to 7 months after the start of treatment, based on the JIA American College of Rheumatology Pediatric criteria for improvement, clinical Juvenile Arthritis Disease Activity Score, and persistence of treatment after 6 months. Subsequently, pooled logistic regression models were performed to include long-term follow-up data. The models were adjusted for risk factors associated with poor treatment response. Dosing was expressed by body weight, body surface area, ideal body weight, fat free mass, and lean body mass. RESULTS: Participants treated with adalimumab (n = 328) and etanercept (n = 437) were included in the analyses (median dose 0.82 mg/kg body weight [interquartile range (IQR) 0.66-1.04] and 0.83 mg/kg body weight [IQR 0.75-0.95], respectively). The majority of analyses did not show a relationship between dose and outcome. Where associations were found, results were conflicting. Alternative dosing characteristics based on ideal body weight, fat free mass, and lean body mass did not result in stronger or more consistent associations. CONCLUSION: This study was not able to confirm our hypothesis that increased dosing of TNF inhibitors results in improved treatment outcomes. Although adjustment was performed for risk factors of impaired treatment response, residual confounding by indication likely explains the negative associations found in this study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".