Brief Report: Causes of Stillbirths in Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: There are few precise or recent estimates of the risk or causes of stillbirths in women with systemic lupus erythematosus (SLE). Thus, we undertook the present study to examine causes of stillbirths in mothers with SLE versus those without SLE. METHODS: The Offspring of SLE Mothers Registry (OSLER) is a large population-based cohort, identified through Quebec's health care databases (1989-2009), including all women who had ≥1 hospitalization for delivery after SLE diagnosis, and a randomly selected control group of women matched for age and year of delivery. We identified stillbirths and ascertained the cause of death as indicated on death certificates. Odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated, and multivariate logistic regression analysis was performed to estimate the risk of stillbirth in women with SLE versus controls. RESULTS: In our cohort, 509 women with SLE had 729 births, including 9 stillbirths, while 5,829 matched controls had 8,541 births, including 47 stillbirths. We observed more stillbirths in mothers with SLE than in controls (1.24% versus 0.55%, difference 0.69% [95% CI 0.03, 1.88]). Women with SLE had an increased risk of stillbirth compared to controls (adjusted OR 2.13 [95% CI 1.02, 4.45]). We also observed a trend toward more stillbirths due to placenta-mediated pregnancy complications in mothers with SLE than in controls (44% [95% CI 14, 79] versus 15% [95% CI 6, 28]). CONCLUSION: Compared to women from the general population, women with SLE have an increased risk of stillbirth. Stillbirths in women with SLE might be more often caused by placenta-mediated pregnancy complications compared to stillbirths in mothers without SLE.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".