Perinatal and Early Infant Outcomes After Bictegravir Exposure in Pregnancy: A Canadian Surveillance Study
Bibliographic record
Abstract
BACKGROUND: Bictegravir (BIC) was recently transitioned from insufficient data in pregnancy to an alternative antiretroviral therapy in pregnancy. Our study aimed to examine the perinatal and early infant outcomes after BIC exposure in pregnancy in Canada. METHODS: Data were obtained from the Canadian Perinatal HIV Surveillance Program for liveborn infants from July 28, 2018, to December 31, 2023. Using univariate analyses, BIC-exposed infants were compared with infants exposed to other antiretroviral regimens. To determine the independent association between preterm births and BIC, we completed a logistic regression analysis adjusting for relevant preterm birth risk factors. RESULTS: Among 1256 infants, 161 infants were exposed to BIC in pregnancy compared with 1095 infants exposed to non-BIC regimens. BIC exposure was categorized as preconception BIC with continued use in pregnancy (n = 81; 52%), preconception BIC with discontinuation in pregnancy (n = 34; 22%), and BIC started in pregnancy (n = 41; 26%). Infants exposed to BIC were more likely born to Indigenous mothers (38% vs. 21%; P < 0.001) linked with injection drug use (28% vs. 14%; P < 0.001). Infants exposed to BIC were more likely born preterm (19.4% vs. 12.9%; P = 0.025). After adjusting for ethnicity, maternal mode of HIV transmission, and viral load at delivery, preterm birth was not associated with BIC exposure (OR: 1.39; 95% CI: 0.78 to 2.49; P = 0.261). There were no between-group differences in maternal HIV viral load at delivery, mode of delivery, small for gestational age, perinatal HIV transmission, or congenital anomalies. CONCLUSIONS: BIC was not independently associated with adverse perinatal and early infant outcomes in the Canadian cohort, supporting recent guideline updates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".