Gestational Diabetes Mellitus Risk in Pregnant Women With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To investigate the risk of gestational diabetes mellitus (GDM) associated with systemic lupus erythematosus (SLE) by comparing pregnancies in women with SLE to general population controls. METHODS: We identified singleton pregnancies among women with SLE and general population controls in the Swedish Medical Birth Register (MBR; 2006-2016), sampled from the population-based Swedish Lupus Linkage (SLINK) cohort (1987-2012). SLE was defined by ≥ 2 International Classification of Diseases (ICD)-coded visits in the National Patient Register (NPR) and MBR, with ≥ 1 visit before pregnancy. GDM was defined by ≥ 1 ICD-coded visit in the NPR or MBR. Glucocorticoid (GC) and hydroxychloroquine (HCQ) dispensations within 6 months before and during pregnancy were identified in the Prescribed Drug Register. Risk ratios (RRs) and 95% CIs of GDM associated with SLE were estimated using modified Poisson regression models, stratified by parity and adjusted for maternal age at delivery, year of birth, and obesity. RESULTS: We identified 695 SLE pregnancies including 18 (2.6%) with GDM and 4644 non-SLE pregnancies including 65 (1.4%) with GDM. Adjusted RRs of GDM associated with SLE were 1.11 (95% CI 0.38-3.27) for first deliveries and 2.03 (95% CI 1.21-3.40) for all deliveries. Among SLE pregnancies, GDM occurred in 7/306 (2.3%) with ≥ 1 GC before and/or during pregnancy, 11/389 (2.8%) without GC, 7/287 (2.4%) with ≥ 1 HCQ before and/or during pregnancy, and in 11/408 (2.7%) without HCQ. CONCLUSION: When looking at all deliveries, SLE was associated with a 2-fold higher risk of GDM. GDM occurrence did not differ by GC or HCQ.
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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 | 0.000 | 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) | 0.001 | 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".