Reproductive outcomes following hydroxychloroquine use for autoimmune diseases: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: The objective of this meta-analysis was to determine whether gestational use of hydroxychloroquine (HCQ) for autoimmune disorders leads to an increase in the risk for adverse pregnancy outcomes. METHODS: MEDLINE, EMBASE, Web of Science, and Cochrane Central Register of Controlled Trials databases were searched from inception to November 21 2014. Studies which reported the outcomes of pregnant women after exposure to HCQ during pregnancy and including a control (unexposed) group were included. Two independent reviewers carried out the review and the quality assessment using the Methodological Index for Non-Randomized Studies (MINORS). A random effects method was used to calculate the odds ratios (OR) for the outcomes. RESULTS: The meta-analysis reported no significant increases in rates of major congenital (OR 1.13, 95% confidence interval (CI) 0.59, 2.17), craniofacial (OR 0.62, 95% CI 0.13, 3.03), cardiovascular (OR 1.06, 95% CI 0.29, 3.86), genitourinary (OR 1.38, 95% CI 0.42, 4.53), nervous system malformations (OR 1.81, 95% CI 0.31, 10.52), stillbirth (OR 0.69, 95% CI 0.35, 1.34), low birth weight (OR 0.69, 95% CI 0.21, 2.27) or prematurity (OR 1.75, 95% CI 0.95, 3.24). The rate of spontaneous abortions, however, was found to be significantly increased in HCQ exposed pregnancies (OR 1.85, 95% CI 1.10, 3.13). No significant heterogeneity was detected among the studies for the evaluated outcomes except prematurity. CONCLUSIONS: Prenatal exposure to HCQ for autoimmune diseases does not appear to increase the risk of adverse pregnancy outcomes except spontaneous abortion rate, which may be associated with the underlying disease activity (bias by indication) and needs further investigation.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.052 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".