HIV incidence among gay men and other men who have sex with men in 2020: where is the epidemic heading?
Bibliographic record
Abstract
The goal to effectively prevent new HIV infections among gay, bisexual, and other men who have sex with men (MSM) is more challenging now than ever before. Despite declines in the late 1990s and early 2000s, HIV incidence among MSM is now increasing in many low- and high-income settings including the US, with young, adolescent, and racial/ethnic minority MSM being among those at highest risk. Potentiating HIV risks across all settings are individual-, network-, and structural-level factors such as stigma and lack of access to pre-exposure prophylaxis (PrEP) and antiretroviral treatment as prevention. To make a sustained impact on the epidemic, a concerted effort must integrate all evidence-based interventions that will most proximally decrease HIV acquisition and transmission risks, together with structural interventions that will support improved coverage and retention in care. Universal HIV treatment, increased access to HIV testing, and daily oral PrEP have emerged as integral to the prevention of HIV transmission, and such efforts should be immediately expanded for MSM and other populations disproportionately affected by HIV. Respect for human rights and efforts to combat stigma and improve access to prevention services are needed to change the trajectory of the HIV pandemic among MSM.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".