The Testing Imperative: Why the US Ending the Human Immunodeficiency Virus (HIV) Epidemic Program Needs to Renew Efforts to Expand HIV Testing in Clinical and Community-Based Settings
Bibliographic record
Abstract
Data from several modeling studies demonstrate that large-scale increases in human immunodeficiency virus (HIV) testing across settings with a high burden of HIV may produce the largest incidence reductions to support the US Ending the HIV Epidemic (EHE) initiative's goal of reducing new HIV infections 90% by 2030. Despite US Centers for Disease Control and Prevention's recommendations for routine HIV screening within clinical settings and at least yearly screening for individuals most at risk of acquiring HIV, fewer than half of US adults report ever receiving an HIV test. Furthermore, total domestic funding for HIV prevention has remained unchanged between 2013 and 2019. The authors describe the evidence supporting the value of expanded HIV testing, identify challenges in implementation, and present recommendations to address these barriers through approaches at local and federal levels to reach EHE targets.
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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 | 0.033 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".