Tenofovir use and pregnancy among women initiating HAART
Bibliographic record
Abstract
OBJECTIVE: Recent studies have raised concerns about a change in rates of pregnancy among HIV-negative women exposed to tenofovir. Here, our objective was to determine among HIV-positive women whether use of tenofovir at HAART initiation or thereafter is associated with subsequent changes in incidence of pregnancy. DESIGN: Analysis of prospectively collected clinical data. METHODS: We used Cox proportional hazards models and logistic regression to estimate hazard ratios and odds ratios for the association of baseline tenofovir use and first incident pregnancy. We used marginal structural Cox models to estimate hazard ratios for the association of current tenofovir use and time to first incident pregnancy. RESULTS: We studied 7275 women, of whom 1199 were initiated on tenofovir-based HAART regimens, and who experienced a total of 894 pregnancies in 17,200 person-years of follow-up. Analyses showed slight reductions in hazards of pregnancy among women who used tenofovir but without sufficient precision to draw strong conclusions. Sensitivity analyses confirmed main results. CONCLUSIONS: Tenofovir may be associated with a lower hazard or rate of pregnancy in women receiving HAART. However, conclusions are limited by low precision, the observational nature of the data, and possible uncontrolled confounding by temporal trends in contraception use and other factors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".