Use of biologics during pregnancy and risk of serious infections in the mother and baby: a Canadian population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between exposure to biologics during pregnancy and serious infections in mothers and infants. DESIGN: Retrospective cohort study. SETTING: Population-based. PARTICIPANTS: Women with one or more autoimmune diseases identified by International Classification of Diseases 9th/10th revision codes in healthcare administrative databases in British Columbia, Canada, who had pregnancies ending in a live or stillbirth between 1 January 2002 and 31 December 2012. Women were defined as exposed if they had at least one biologic prescription during pregnancy, and infants born to these women were considered exposed in utero. Disease-matched women with no biologics prescriptions during pregnancy, and their infants, comprised the unexposed groups. PRIMARY OUTCOME MEASURES: Serious infections requiring hospitalisation. RESULTS: Over the 10-year study period, there were 6218 women (8607 pregnancies) who had an autoimmune disease diagnosis, of which 90 women were exposed to biologics during pregnancy, with 100 babies born to these women. Among women exposed to biologics during pregnancy, occurrence of serious postpartum infections were low, ranging from 0% to 5%, depending on concomitant exposures to immunosuppressants. In multivariable models using logistic regression, the OR for the association of biologics exposure with serious maternal postpartum infections was 0.79 (95% CI 0.24 to 2.54). In infants exposed to biologics in utero, occurrence of serious infections during the first year of life ranged from 0% to 7%, depending on concomitant exposures to immunosuppressants in utero. Multivariable models showed no association between biologics exposure in utero and serious infant infections (OR 0.56, 95% CI 0.17 to 1.81). CONCLUSIONS: These population-based data suggest that the use of biologics by women with autoimmune diseases during pregnancy is not associated with an increased risk of serious infections in mothers, during post partum or in infants during the first year of life.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".