Effect of pregnancy on scleroderma progression
Bibliographic record
Abstract
Objective: To explore the trajectory of scleroderma disease activity in women who experienced a pregnancy after systemic sclerosis diagnosis compared to nulliparous women. Methods: We analyzed data from the Canadian Scleroderma Research Group registry by identifying nulliparous women and women with ⩾1 pregnancy after systemic sclerosis diagnosis. Patient characteristics were compared between groups at registry entry. Controlling for age, smoking, and time since systemic sclerosis diagnosis, generalized estimating equations tested the effect of pregnancy on force vital capacity, diffusing capacity of the lungs for carbon monoxide, right ventricular systolic pressure, glomerular filtration rate, antibody status, active digital ulcers, physician global assessment of activity, and severity over 9 years. Results: At registry entry, numbers of women in the nulliparous and pregnancy after systemic sclerosis diagnosis groups were 153 and 45, respectively. Corresponding numbers at 6 and 9 years were 48 and 21, and 18 and 9, respectively. The prevalence of anti-topoisomerase positivity was 18.3% in nulliparous and 12.5% in pregnancy after systemic sclerosis diagnosis. Baseline differences included mean (Standard deviation) age of diagnosis (nulliparous: 38.8 (14.0), pregnancy after systemic sclerosis diagnosis: 22.6 (6.8) years, p < 0.001), disease duration (nulliparous: 9.6 (8.9), pregnancy after systemic sclerosis diagnosis: 21.9 (9.6) years; p < 0.001), and inflammatory arthritis (nulliparous: 41 (28%), pregnancy after systemic sclerosis diagnosis: 22 (49%), p = 0.009). There were no significant differences between groups in the change of any outcomes over time. Conclusion: Results demonstrated that having ⩾1 pregnancy after systemic sclerosis diagnosis did not appear to significantly impact long-term renal, respiratory, or global function outcomes. While this offers a hopeful message to systemic sclerosis patients planning a pregnancy, physicians and patients should remain vigilant for potential post-partum complications.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".