Real-world trends in rheumatic disease rates among pregnancies in Ontario, Canada: A repeated cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: Despite increasing rheumatic disease prevalence among female individuals in Ontario, Canada, research is limited on whether this has translated to an increased proportion of pregnancies with rheumatic diseases. This study aimed to assess rheumatic disease rates among pregnant individuals in Ontario, Canada. METHODS: Using healthcare administrative claims databases from ICES, the proportion of pregnancies with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), and ankylosing spondylitis (AS) from April 2011 to March 2021 were examined. The Mann-Kendall rank correlation test was used to test for trends in overall and individual rheumatic disease rates across the study period. RESULTS: In total, 1,408,100 pregnancies were recorded from fiscal years 2011-2020 in Ontario. Of these, 4,392 involved patients with rheumatic diseases. The overall rheumatic disease rate among pregnant individuals increased by 24% from 28.3 to 35.2 pregnancies per 10,000 over the study period (p = 0.0157). RA rates increased 22% from 11.1 to 13.5 per 10,000 (p = 0.0056), while PsA rates increased 111% from 0.9 to 1.9 per 10,000 (p = 0.0157). SLE, AS, and multiple rheumatic disease rates did not increase significantly. CONCLUSION: The proportion of individuals with rheumatic diseases pursuing pregnancy in Ontario has increased, perhaps aided by advancements in disease diagnostics and treatment strategies. Further research is needed to link antirheumatic drug safety to pregnancy outcomes and explore other factors that may influence pregnancy rates among this patient population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".