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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".