Is reaching 90–90–90 enough to end AIDS? Lessons from Amsterdam
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although cities present opportunities for infectious pathogens such as HIV to spread, public health infrastructure within these cities also provides opportunities to design effective approaches to eliminate transmission of these pathogens. The HIV Transmission Elimination AMsterdam (H-TEAM) Initiative, a consortium of relevant stakeholders involved in HIV prevention and care, designed an integrated approach to curb the HIV epidemic in Amsterdam, including providing preexposure prophylaxis (PrEP), increasing awareness of acute HIV infection, offering same-day test and treat, and improving indicator disease-driven HIV testing. RECENT FINDINGS: In 2013, approximately 230 people in Amsterdam were newly diagnosed with HIV, largely belonging to one of two key affected populations, namely MSM and people with a migration background. Since the start of H-TEAM in 2014, a decrease in new diagnoses was observed (130 in 2017), with an increasing proportion of MSM who had been diagnosed with a recent infection. SUMMARY: The H-TEAM shows that a city-based concerted effort is feasible. However, major challenges remain, such as reducing the number of late HIV diagnoses, and identifying and providing appropriate services to a diminishing group of individuals who are likely the source of transmission.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".