Outcomes of children born to mothers with systemic lupus erythematosus exposed to hydroxychloroquine or azathioprine
Bibliographic record
Abstract
OBJECTIVES: HCQ and AZA are used to control disease activity and reduce risk of flare during pregnancy in patients with SLE. The aim of this study was to determine the outcomes of children born to mothers with SLE exposed to HCQ or AZA during pregnancy and breast-feeding. METHODS: Women attending UK specialist lupus clinics with children ≤17 years old, born after SLE diagnosis, were recruited to this retrospective study. Data were collected using questionnaires and from clinical record review. Factors associated with the outcomes of low birth weight and childhood infection were determined using multivariable mixed-effects logistic regression models. RESULTS: We analysed 284 live births of 199 mothers from 10 UK centres. The first pregnancies of 73.9% of mothers (147/199) were captured in the study; (60.4%) (150/248) and 31.1% (87/280) children were exposed to HCQ and AZA, respectively. There were no significant differences in the frequency of congenital malformations or intrauterine growth restriction between children exposed or not to HCQ or AZA. AZA use was increased in women with a history of hypertension or renal disease. Although AZA was associated with low birth weight in univariate models, there was no significant association in multivariable models. In adjusted models, exposure to AZA was associated with increased reports of childhood infection requiring hospital management [odds ratio 2.283 (1.003, 5.198), P = 0.049]. CONCLUSIONS: There were no significant negative outcomes in children exposed to HCQ in pregnancy. AZA use was associated with increased reporting of childhood infection, which warrants further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".