Reduced HIV Risk-Taking and Low HIV Incidence After Enrollment and Risk-Reduction Counseling in a Sexually Transmitted Disease Prevention Trial in Nairobi, Kenya
Bibliographic record
Abstract
There is an urgent need in sub-Saharan Africa to develop more effective methods of HIV prevention, including improved strategies of sexually transmitted infection (STI) prevention or an HIV vaccine. The efficacy of these strategies may be tested through clinical trials within cohorts at high risk for STI and HIV, such as female commercial sex workers. For ethical reasons, standard HIV prevention services, including access to free condoms, risk-reduction counseling, and STI therapy, will generally be offered to all study subjects. Because study subjects would often not otherwise have access to these prevention services, it is possible that enrollment in such clinical trials will itself reduce incidence rates of STI and HIV below expected levels, reducing the power to test the efficacy of the randomized intervention. We show that the provision of standard HIV prevention services as part of a randomized STI/HIV prevention trial is temporally associated with a dramatic reduction in sexual risk-taking, and that this reduction is directly associated with reduced STI incidence. This finding should be considered in the design of clinical trials with an endpoint of HIV incidence, in particular HIV preventive vaccine trials.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".