The association of lupus nephritis with adverse pregnancy outcomes among women with lupus in North America
Bibliographic record
Abstract
OBJECTIVES: We evaluated the association of lupus nephritis (LN) and adverse pregnancy outcomes in prospective cohorts of pregnant women with SLE (systemic lupus erythematosus). METHODS: We conducted a patient-level pooled analysis of data from three cohorts of pregnant women with SLE. Pooled logistic regression models were used to evaluate the association of LN and adverse pregnancy outcomes. Odds ratios and 95% confidence intervals were calculated using a fixed effect model by enrolling cohort. RESULTS: The pooled cohort included 393 women who received care at clinics in the United States and Canada from 1995 to 2015. There were 144 (37%) women with a history of LN. Compared to women without LN, those with LN had higher odds of fetal loss (OR: 1.90; 95% CI: 1.01, 3.56) and preeclampsia (OR: 2.04; 95% CI: 1.01, 4.13). Among the 31 women with active nephritis (defined as urine protein ≥ 0.5 g/24 h) there was a higher odds of poor pregnancy outcome (OR: 3.08; 95% CI: 1.31, 7.23) and fetal loss (OR: 6.29; 95% CI: 2.52, 15.70) compared to women without LN. CONCLUSIONS: In this pooled cohort of women with SLE, a history of LN was associated with fetal loss and preeclampsia. Active nephritis was associated with poor pregnancy outcome and fetal loss.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".