The association between heterosexual anal intercourse and HIV acquisition in three prospective cohorts of women
Bibliographic record
Abstract
Abstract Receptive anal intercourse (RAI) may substantially increase HIV acquisition risk per sex act compared to receptive vaginal intercourse (RVI). To understand how levels of RAI change over time and evaluate the impact of exposure definitions for RAI on HIV incidence, we analysed three prospective HIV cohorts of women: RV217, MTN-003 (VOICE), and HVTN 907. At baseline 16.0% (RV 217), 17.5% (VOICE) of women reported RAI in the past 3 months and 27.3% (HVTN 907) in the past 6 months, with RAI declining during follow-up by around 3-fold. Hazard ratios, adjusted for potential confounders (aHR), indicate that reporting RAI at baseline increased HIV incidence in the three cohorts: 1.1 (95% Confidence interval: 0.8-1.5) for VOICE, aHR of 3.3 (1.6-6.8) for RV 217, and 1.9 (0.6-6) for HVTN 907. Using time-varying exposure definition slightly increased the estimated association for VOICE (aHR=1.2; 0.9-1.6), however reporting >30% RAI sex acts during VOICE follow-up was not associated with higher HIV incidence (aHR=0.7 (0.4-1.1)). Women who always reported RAI during follow-up where also at increased HIV acquisition risk. Overall, we found that precisely estimating RAI and HIV association after multiple RVI/RAI exposures is sensitive to RAI exposure definitions and may be influenced by measurement errors.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".