Impact of the Season of Birth on the Development of Juvenile Idiopathic Arthritis in the United States: A Nationwide Registry-based Study
Bibliographic record
Abstract
Objective Autoimmune disorders result from the interplay of genetic and environmental factors. Many autoimmune disorders are associated with specific seasons of birth, implicating a role for environmental determinants in their etiopathology. We investigated if there is an association between the season of birth and the development of juvenile idiopathic arthritis (JIA). Methods Birth data from 10,913 children with JIA enrolled at 62 Childhood Arthritis and Rheumatology Research Alliance Registry sites was compared with 109,066,226 US births from the same period using a chi-square goodness-of-fit test. Season of birth of the JIA cohort was compared to the US population estimate using a 2-sided 1-sample test for a binomial proportion and corrected for multiple comparisons. Secondary analysis was performed for JIA categories, age of onset, and month of birth. Results A greater proportion of children with JIA were born in winter (January–March) compared to the US general population (25.72% vs 24.08%; correctedP< 0.0001). This observation was also true after stratifying for age of onset (≤ or > 6 yrs). When analyzed by the month of birth, a greater proportion of children with JIA were born in January compared to the US population (9.44% vs 8.13%; correctedP< 0.0001). Conclusion Relative to the general population, children with JIA are more often born in the winter, and specifically in the month of January. These observations support the hypothesis that seasonal variations in exposures during the gestational and/or early postnatal periods may contribute to development of JIA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".