Decrease in pregnancy loss rates in patients with systemic lupus erythematosus over a 40-year period.
Bibliographic record
Abstract
OBJECTIVE: To determine if there has been a statistically significant change in pregnancy loss and preterm delivery rates in patients with systemic lupus erythematosus (SLE). METHODS: We analyzed the pregnancy outcomes of our SLE patients over the past 3 years and reviewed the literature over the past 40 years. We extracted pregnancy loss and preterm delivery data from reports of postdiagnosis SLE pregnancies. Studies were grouped into 5-year periods and weighted according to sample size. Group means, calculated for each study period, were plotted using linear regression to determine significance, and compared with population norms for the same periods. RESULTS: The rate of loss in SLE pregnancies over the past 40 years decreased from a mean of 43% in 1960-1965 to 17% in 2000-2003 (r2 = 0.648). This approximates the pregnancy loss rate in the general US population. Preterm deliveries were not uniformly reported and were rarely stratified into spontaneous or physician-initiated. Prior to 1980, it was not possible to derive group means for each time period. From 1980 to 2002, however, there was a trend toward a decrease in preterm births in SLE pregnancies, although they continue to be more frequent in SLE than in the general population. CONCLUSION: Improvements in disease management and perinatal monitoring have resulted in a significant decrease in pregnancy loss in SLE over the last 40 years and a trend toward decreased preterm deliveries over the last 20 years in comparison to the general population. These advances highlight the importance of collaboration between rheumatologists and perinatologists. Given these data, the description of SLE-associated pregnancy could be revised to reflect a more positive prognosis for mother and fetus.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".