Fecal Microbiota in Untreated Children With Juvenile Idiopathic Arthritis: A Comparison With Healthy Children and Healthy Siblings
Bibliographic record
Abstract
OBJECTIVE: Changes in the composition of gut microbiota have been suggested to be associated with juvenile idiopathic arthritis (JIA). The objective in this study was to investigate if the diversity and composition of the fecal microbiota differed between children with JIA and healthy controls (HCs), and if the microbiota differed between children with JIA and their healthy siblings. METHODS: In this multicenter, case-control study, fecal samples were collected from 75 children with JIA and 32 HCs. Eight of the HCs were siblings to 8 children with JIA, and they were compared only pairwise with their siblings. The microbiota was determined using sequencing amplicons from the V3 and V4 regions of the 16S rRNA gene. Alpha diversity, community composition of microbiota, and relative abundances of taxa were compared between children with JIA and healthy unrelated controls as well as between children with JIA and healthy siblings. RESULTS: Our data revealed no significant differences in α-diversity or community composition of microbiota between children with JIA, healthy unrelated controls, or healthy siblings. Analyses of relative abundances of phyla, families, and genera identified trends of differing abundances of some taxa in children with JIA, in comparison with both HCs and healthy siblings, but none of these findings were significant after adjustment for multiple comparisons. CONCLUSION: There were no significant differences in the composition of fecal microbiota in children with JIA compared with HCs. The composition of microbiota in children with JIA did not differ significantly from that in their healthy siblings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".