The Clinical Utility of Measuring Complement and Anti-dsDNA Antibodies During Pregnancy in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: The importance of low complement and anti-dsDNA during pregnancy in patients with systemic lupus erythematosus (SLE) is poorly defined. We investigated the effect of these laboratory tests and clinical SLE activity on pregnancy outcomes. METHODS: We conducted a study of all pregnancies in patients with SLE followed from 1986 to 2002 in a cohort of patients with SLE. At each visit, the physician's estimate of activity (PEA), complement, and anti-dsDNA antibody were measured. We assessed the combination of moderate to severe SLE clinical activity (defined as PEA ≥ 2) and these serologic measurements on pregnancy outcomes. Pregnancies electively terminated were excluded from our study. RESULTS: Regardless of SLE activity, low complement or positive anti-dsDNA in the second trimester was associated with a higher rate of pregnancy loss and preterm birth. Patients with the combination of either high clinical activity of SLE and low complement or positive anti-dsDNA had the highest rate of pregnancy loss and preterm birth. CONCLUSION: Women with the combination of high clinical activity with serologic markers of SLE activity are at highest risk for pregnancy loss and preterm delivery. While hypocomplementemia and positive anti-dsDNA alone are predictive of poor pregnancy outcomes in the second trimester, the risks are far higher for the women in whom this is coupled with clinically active SLE.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".