Lupus Low Disease Activity State Achievement Is Important for Reducing Adverse Outcomes in Pregnant Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine the frequency and risk factors of complications during pregnancy in women with systemic lupus erythematosus (SLE). METHODS: The medical records of patients with SLE and age-matched controls at Ajou University Hospital were collected. Clinical features and pregnancy complications in women with SLE were compared to those of the controls. Multivariate logistic regression analysis was performed to determine the predictors of adverse maternal and fetal outcomes. RESULTS: We analyzed 163 pregnancies in patients with SLE and 596 pregnancies in the general population; no significant differences regarding demographic characteristics were noted. Patients with SLE experienced a higher rate of stillbirth (OR 13.2), preeclampsia (OR 4.3), preterm delivery (OR 2.8), intrauterine growth retardation (OR 2.5), admission to neonatal intensive care unit (OR 2.2), and emergency cesarean section (OR 1.9) than the control group. Multivariate regression analysis revealed that thrombocytopenia, low complement, high proteinuria, high SLE Disease Activity Index (SLEDAI), low Lupus Low Disease Activity State (LLDAS) achievement rate, and high corticosteroid (CS) dose were associated with adverse pregnancy outcomes. In the receiver-operating characteristic curve analysis, the optimal cutoff value for the cumulative and mean CS doses were 3500 mg and 6 mg, respectively. CONCLUSION: Pregnant women with SLE have a higher risk of adverse pregnancy outcomes. Pregnancies are recommended to be delayed until achieving LLDAS and should be closely monitored with the lowest possible dose of CS.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".