Pediatric Sarcoidosis: Retrospective Analysis of Biopsy-Proven Patients
Bibliographic record
Abstract
Objective To describe the phenotype, disease course, and treatment of a large cohort of children with sarcoidosis. Methods Patients with biopsies consistent with sarcoidosis, performed between 2010 and 2020, were included in this study. Patients’ notes were reviewed retrospectively. Children with disease onset before 5 years of age were compared with older children. Regression analysis was performed to determine predictors of treatment outcome. Results In total, 48 children with a mean age at diagnosis of 9.5 years, with a male to female ratio of 0.71, were identified. In total, 72% of the children were of Black race and 94% had multiorgan disease, with an average of 4.8 organs involved, most commonly lymph nodes (65%), skin (63%), and eyes (60%). Laboratory findings of note included raised serum calcium in 23% of patients and raised angiotensin-converting enzyme in 76% of patients. Out of 14 patients tested, 6 had mutations inNOD2. In total, 81% of patients received systemic steroids and 90% received conventional disease-modifying antirheumatic drugs (DMARDs); in 25% of patients, a biologic was added, mostly anti–tumor necrosis factor (anti-TNF). Although most patients could be weaned off steroids (58%), most remained on long-term DMARDs (85%). Children under the age of 5 years presented more often with splenomegaly (P= 0.001), spleen involvement (P= 0.003), and higher C-reactive protein (P= 0.10). Weight loss was more common in adolescents (P= 0.006). Kidney (P= 0.004), eye (P= 0.005), and liver involvement (P= 0.03) were more common in Black patients. Regression analysis identified no single factor associated with positive treatment outcomes. Conclusion Multiorgan involvement, response to steroids, and chronic course are hallmarks of pediatric sarcoidosis. The phenotype significantly varies by age and race. Where conventional DMARDs were not efficacious, the addition of an anti-TNF agent was beneficial.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".