Repeated Exposure to Antibiotics in Infancy: A Predisposing Factor for Juvenile Idiopathic Arthritis or a Sign of This Group’s Greater Susceptibility to Infections?
Bibliographic record
Abstract
OBJECTIVE: Previous exposure to antibiotics has been associated with the pathogenesis of several autoimmune diseases. Our objective was to explore whether childhood exposure to antibiotics would be associated with the risk of developing juvenile idiopathic arthritis (JIA). METHODS: The material was collected from national registers containing all children born in 2000-2010 in Finland and diagnosed with JIA by the end of December 2012 (n = 1298) and appropriate controls (n = 5179) matched for age, sex, and place of birth. All purchases of antibiotics were collected from birth until the index date (i.e., the date of special reimbursement for JIA medications). A conditional logistic regression was performed to evaluate the association between the exposure to antibiotics and the risk of JIA. RESULTS: The risk of JIA increased with the number of antibiotic purchases from birth to the index date: for ≥ 1 purchases versus none, OR 1.6, 95% CI 1.3-1.9 with an upward trend in OR (p < 0.001). Antibiotic groups lincosamides and cephalosporins showed the strongest association with JIA (OR 6.6, 95% CI 3.7-11.7, and OR 1.6, 95% CI 1.4-1.8, respectively). Overall exposure to antibiotics before 2 years of age was associated with an increased risk of JIA (OR 1.4, 95% CI 1.2-1.6), with the trend test of OR (p < 0.001). CONCLUSION: Previous early and repeated exposure to antibiotics may predispose individuals to develop JIA. Alternatively, the apparent association may reflect shared susceptibility to infections and JIA.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".