Repeated Exposure to Antibiotics in Infancy: A Predisposing Factor for Juvenile Idiopathic Arthritis or a Sign of This Group’s Greater Susceptibility to Infections?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it