Risk of congenital anomalies in infants born to women with autoimmune disease using biologics before or during pregnancy: a population-based cohort study.
Bibliographic record
Abstract
OBJECTIVES: To determine the association between perinatal biologic use and congenital anomalies in women with autoimmune disease. METHODS: We linked population-based administrative health data including information on all medications with a perinatal registry in British Columbia, Canada. Women with one or more autoimmune diseases who had pregnancies between January 1st, 2002 and December 31st, 2012 were included. Exposure to biologics was defined as having at least one biologic prescription 3 months before conception or during the first trimester of pregnancy. Each exposed pregnancy was matched with five unexposed pregnancies using high dimensional propensity scores (HDPS). Logistic regression modelling was used to evaluate the association between biologics use and congenital anomalies. RESULTS: The HDPS-matched cohort included 117 pregnancies (107 women) exposed to biologics, and 585 pregnancies (562 women) that were not exposed to biologics during the period of interest; 6% of newborns had ≥1 congenital anomalies at birth, in the exposed and unexposed groups. There were no obvious patterns with regards to the congenital anomalies observed in the biologics exposed group. In primary analysis, the OR for the association between biologic exposure and congenital anomalies was 1.06 (95%CI 0.46-2.47). Secondary and sensitivity analyses did not change the results appreciably. CONCLUSIONS: These population-based data suggest that the use of biologics before and during pregnancy is not associated with an increased risk of congenital anomalies.
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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 | 0.000 |
| 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 | 0.001 |
| 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".