Meta-Analysis Dataset: Effectiveness of HIV Pre-Exposure Prophylaxis (PrEP) Across 100 Studies (2000–2025)
Bibliographic record
Abstract
This dataset presents a comprehensive meta-analysis of HIV pre-exposure prophylaxis (PrEP) effectiveness across 100 studies conducted between 2000 and 2025. The included studies represent a mix of randomized controlled trials (62 studies) and observational studies (38 studies) conducted globally, encompassing diverse populations at heightened risk of HIV infection: • Men who have sex with men (MSM) (42 studies) • Serodiscordant couples (18 studies) • People who inject drugs (PWID) (12 studies) • High-risk heterosexuals (28 studies) PrEP interventions covered in the analysis include both oral formulations (84 studies) and long-acting injectable cabotegravir (CAB) (16 studies). The study adhered to PRISMA guidelines for systematic review and meta-analysis. A total of 2,134 records were initially identified, with 100 studies meeting inclusion criteria after full-text screening. Outcomes assessed include: • HIV incidence reduction (relative risk, confidence intervals) • PrEP adherence metrics (pill count, electronic monitoring, plasma levels, self-report) • Safety and tolerability outcomes (GI events, renal function, bone density, injection-site reactions) • Risk of bias assessment using the Cochrane tool (for RCTs) and Newcastle-Ottawa Scale (for observational studies). Findings highlight consistently high effectiveness among MSM (≈90% risk reduction with high adherence), substantial benefit in serodiscordant couples (≈75%), moderate protection among PWID (≈49%), and variable efficacy in heterosexual populations (46–77%). Adherence emerged as a critical determinant of effectiveness. This dataset provides a modular synthesis matrix and detailed evidence tables that can be used by researchers, clinicians, and policymakers to optimize PrEP implementation strategies, improve adherence support, and inform public health guidelines.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".