Body mass index associated with glucocorticoid-related weight gain in children with rheumatic disease on high-dose prednisone
Bibliographic record
Abstract
OBJECTIVES: To evaluate the relationship between patient variables that affect pharmacokinetic variability with glucocorticoid (GC)-related weight gain within the first 12 months of starting prednisone therapy. METHODS: We conducted a retrospective chart review of children aged <18 years diagnosed with rheumatic disease treated with moderate to high-dose prednisone therapy at a single Canadian paediatric academic hospital between January 1, 2010, and December 31, 2020. Using a binary logistic regression, eGFR, initial Body Mass Index (BMI), transaminitis and albumin were evaluated as predictors of GC-related obesity (defined as weight gain greater than 20% and BMI z-score ≥1.88 or >95%ile after 12 months of treatment) was evaluated. RESULTS: Data for sixty-two patients were included in this analysis with 18 (29%) systemic JIA, (6%) other JIA subtypes, 22 (36%) SLE, and 8 (13%) JDM patients, and the remaining patients diagnosed with connective tissue disease and other inflammatory disorders (n=10, 16%). Eighteen (29%) patients met criteria for GC-induced obesity by 12 months of therapy. Greater BMI z-score prior to initiation of GC-therapy was associated with greater risk of developing GC-induced obesity (OR=2.35, 95%CI=1.39-3.96, p<0.001). CONCLUSIONS: Greater BMI was a predictor of severe GC-related obesity for children with rheumatic disease requiring moderate to high-dose prednisone therapy. Further work is required to determine methods for individualised prednisone dosing, and interventions to mitigate risk for weight gain.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".