A Group-Based Trajectory Model for Changes in Pre-Exposure Prophylaxis and Condom Use Among Men Who Have Sex with Men Participating in the ANRS IPERGAY Trial
Bibliographic record
Abstract
Pre-exposure prophylaxis (PrEP) is one of the most important milestones in HIV prevention for high-risk HIV-negative people. The introduction of PrEP into the HIV prevention package has raised the question of possible risk compensation, and in particular, a reduction in condom use. Although open-label extension (OLE) studies have enabled investigators to examine risk compensation in more real-life settings, results to date have been inconclusive regarding PrEP-induced behavioral changes. We aimed to identify behavioral trajectories over time for PrEP and condom use, and to investigate the link between these trajectories. Group-based trajectory modeling using logistic specification was implemented. Data of the 332 individuals participating in the double-blind and OLE phases of ANRS IPERGAY were used. Four distinct group trajectories were identified: 49.7% of the participants had “systematic adherence to PrEP,” 19% had “high adherence to PrEP, 15.3% had “declining adherence to PrEP,” and 16% had “low adherence to PrEP.” Two condom trajectories were identified: 53% (n = 176) of the 332 participants were “low-level users” of condoms, and 47% were “high-level users.” Among the former, 24.4% also belonged to the “low adherence to PrEP” group. This most-at-risk group represented 13% of all the participants. The effectiveness of offering sexual activity-based PrEP as part of the French HIV prevention package in the ANRS IPERGAY clinical trial is demonstrated by the relatively small proportion of participants that offset their low-level condom use with PrEP adherence. The results from this work may help in the future development of tailored prevention programs for high-risk HIV-negative people.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".