Dolutegravir- Versus Efavirenz-Based Treatment in Pregnancy: Impact on Red Blood Cell Folate Concentrations in Pregnant Women and Their Infants
Bibliographic record
Abstract
BACKGROUND: In the International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) 2010/VESTED study, pregnant women were randomized to initiate dolutegravir (DTG) + emtricitabine (FTC)/tenofovir alafenamide (TAF), DTG + FTC/tenofovir disoproxil fumarate (TDF), or efavirenz (EFV)/FTC/TDF. METHODS: We assessed red blood cell (RBC) folate concentrations at maternal study entry and delivery, and infant birth. RBC folate outcomes were (1) maternal change entry to delivery (trajectory), (2) infant, and (3) ratio of infant-to-maternal delivery. Generalized estimating equation models for each log(folate) outcome were fit to estimate adjusted geometric mean ratio (Adj-GMR)/GMR trajectories (Adj-GMRTs) of each arm comparison in 340 mothers and 310 infants. RESULTS: Overall, 90% of mothers received folic acid supplements and 78% lived in Africa. At entry, median maternal age was 25 years, gestational age was 22 weeks, CD4 count was 482 cells/μL, and log10 HIV RNA was 3 copies/mL. Entry RBC folate was similar across arms. Adj-GMRT of maternal folate was 3% higher in the DTG + FTC/TAF versus EFV/FTC/TDF arm (1.03 [95% confidence interval {CI}, 1.00-1.06]). The DTG + FTC/TAF arm had an 8% lower infant-maternal folate ratio (0.92 [95% CI, .78-1.09]) versus EFV/FTC/TDF. CONCLUSIONS: Results are consistent, with no clinically meaningful differences between arms for all RBC folate outcomes, and they suggest that cellular uptake of folate and folate transport to the infant do not differ in pregnant women starting DTG- versus EFV-based antiretroviral therapy. CLINICAL TRIALS REGISTRATION: NCT03048422.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".