How much could a microbicide's sexually transmitted infection efficacy contribute to reducing HIV risk and the level of condom use needed to lower risk? Model estimates
Bibliographic record
Abstract
OBJECTIVE: This study explores the potential contribution of a microbicide's sexually transmitted infection (STI) efficacy in reducing a female sex worker's (FSW) risk of STI and HIV infection. The study then investigates whether the threshold for the reduction in condom use following microbicide introduction that can be tolerated without increasing HIV risk is affected by STI efficacy. METHODS: A dynamic model describing the transmission of a bacterial STI between FSW and their clients was coupled with a static HIV model. The model uses data from Cotonou, Benin (1998-9), for illustration, to estimate the change in risk following the introduction of 50% HIV efficacious microbicides of different STI efficacies, used in 50% of sex acts when a condom is not used. The condom migration thresholds were estimated. The degree to which the findings are influenced by STI prevalence was explored. RESULTS: For highly transmissible STI, there is a non-monotonic relationship between STI prevalence and microbicide impact on HIV with the relative reduction in HIV risk first increasing, due to the proportion of HIV risk attributable to the STI increasing, but then decreasing at high prevalences as the STI becomes harder to control. A less transmissible STI can still be impacted upon with a moderate/high STI efficacy microbicide even at high STI prevalences. This relationship is also reflected in the condom migration thresholds. CONCLUSIONS: A microbicide's STI efficacy may have a substantial impact on STI and HIV incidence among high-risk groups. The variation in the condom migration thresholds for different STI efficacies and STI prevalences may be difficult to measure accurately.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".