A Systematic Review of the Quality of Trials Evaluating Biomedical HIV Prevention Interventions Shows That Many Lack Power
Bibliographic record
Abstract
PURPOSE: Several randomized, controlled trials (RCTs) have tested strategies to prevent sexual acquisition of HIV infection, but their quality has been variable. We aimed to identify, describe, and evaluate the quality of RCTs studying biomedical interventions to prevent HIV acquisition by sexual transmission. METHOD: We conducted a systematic review to identify all RCTs evaluating the efficacy of biomedical HIV prevention interventions. We assessed seven generic and content-specific quality components important in HIV prevention trials, factors influencing study power, co-interventions provided, and trial ethics. RESULTS: We identified 26 eligible RCTs. The median number of quality components judged to be in adequate or unclear was 3 (range 1-4) in 1992-1998, 3 (range 1-4) in 1999-2003, and 0 (range0-2) in 2004-2008 (p < .001). Common problems that may have biased results included low retention (median 84%), poor adherence to interventions requiring on going use (median < or =78%), and lower HIV incidence than expected a priori (in 8 of 11 trials where evaluable). CONCLUSION: Reporting of trials of biomedical HIV prevention interventions has improved over time. However, quality improvement is needed in several key areas that influence study power, including participant retention, adherence to interventions, and estimation of expected HIV incidence.
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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 | 0.286 | 0.685 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".