Targeting screening and social marketing to increase detection of acute HIV infection in men who have sex with men in Vancouver, British Columbia
Bibliographic record
Abstract
OBJECTIVES: The contribution of acute HIV infection (AHI) to transmission is widely recognized, and increasing AHI diagnosis capacity can enhance HIV prevention through subsequent behavior change or intervention. We examined the impact of targeted pooled nucleic acid amplification testing (NAAT) and social marketing to increase AHI diagnosis among men who have sex with men (MSM) in Vancouver. DESIGN: Observational study. METHODS: We implemented pooled NAAT following negative third-generation enzyme immunoassay (EIA) testing for males above 18 years in six clinics accessed by MSM, accompanied by two social marketing campaigns developed by a community gay men's health organization. We compared test volume and diagnosis rates for pre-implementation (April 2006-March 2009) and post-implementation (April 2009-March 2012) periods. After implementation, we used linear regression to examine quarterly trends and calculated diagnostic yield. RESULTS: After implementation, the AHI diagnosis rate significantly increased from 1.03 to 1.84 per 1000 tests, as did quarterly HIV test volumes and acute to non-acute diagnosis ratio. Of the 217 new HIV diagnoses after implementation, 54 (24.9%) were AHIs (25 detected by pooled NAAT only) for an increased diagnostic yield of 11.5%. The average number of prior negative HIV tests (past 2 years) increased significantly for newly diagnosed MSM at the six study clinics compared to other newly diagnosed MSM in British Columbia, per quarter. CONCLUSION: Targeted implementation of pooled NAAT at clinics accessed by MSM is effective in increasing AHI diagnoses compared to third-generation EIA testing. Social marketing campaigns accompanying pooled NAAT implementation may contribute to increasing AHI diagnoses and frequency of HIV testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".