Preexposure prophylaxis on-demand dramatically reduces HIV incidence in MSM
Bibliographic record
Abstract
Recently, multiple clinical trials have shown that preexposure prophylaxis (PrEP) is an effective means of preventing HIV infection – but only if individuals adhere to the treatment, which involves a daily dose of drugs. In a recent issue of the New England Journal of Medicine (2015; 373: 2237–2246), Jean-Michel Molina of the Hôpital Saint-Louis and colleagues reported the results of IPERGAY, a randomized controlled trial that assessed a different PrEP dosage system: two pills 2–24 h before sex, a third pill 24 h after the first pill, and a fourth pill 24 h after that. In MSM, the group being studied, this treatment was associated with an 86% reduction in new HIV infections, one of the largest reported to date for PrEP. Jared Baeten of the University of Washington comments, ‘While the Molina et al. results are just one study, they are pretty powerful and are backed up by animal model data that suggested an on-demand approach would work in the first place.’ Cécile Tremblay of the Université de Montréal observes, ‘We have already seen an effect of the IPERGAY results on PrEP uptake. These results validated the efficacy of PrEP and increased confidence in both patients and doctors that the approach was effective. On-demand PrEP gives more flexibility and options to patients.’ Molina explains that in some countries, such as France, both daily and on-demand PrEP regimens have already been approved for MSM, and the European AIDS Clinical Society guidelines for PrEP include both the options. On-demand dosing is proving popular in some places: Tremblay notes that in Montreal, 20–30% of patients on PrEP now take it on-demand. Robert Grant of the University of California, San Francisco, observes, ‘on-demand dosing will be best suited for people who plan and schedule sexual activity,’ though he adds, ‘I doubt there will be much influence (of the study) in the United States, where both the FDA and the CDC recommend daily use of PrEP.’ He also emphasizes that on-demand dosing is unproven for women and trans men exposed to HIV by vaginal intercourse. Baetan concludes, ‘People want and need options for protecting themselves from HIV, and an on-demand strategy might work for some, while others will prefer a more fixed routine. As a scientist, prescriber, and policymaker, I think we have to do our best to give people effective options that they can use.’ Acknowledgements Conflicts of interest There are no conflicts of interest.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".