Addressing the Achilles' Heel in the HIV Care Continuum for the Success of a Test-and-Treat Strategy to Achieve an AIDS-Free Generation
Bibliographic record
Abstract
Mathematical models and recent data from ecological, observational, and experimental studies show that antiretroviral therapy (ART) is effective for both treatment and prevention of HIV, validating the treatment as prevention (TasP) approach. Data from a variety of settings, including resource-rich and -limited sites, show that patient attrition occurs at each stage of the human immunodeficiency virus (HIV) treatment cascade, starting with the percent unaware of their HIV infection in a population and linkage to care after diagnosis, assessment of ART readiness, receipt of ART, and finally long-term virologic suppression. Therefore, in order to implement TasP, we must first define practical and effective linkage to care, acceptability of treatment, and adherence and retention monitoring strategies, as well as the cost-effectiveness of such strategies. Ending this pandemic will require the combination of political will, resources, and novel effective interventions that are not only feasible and cost effective but also likely to be used in combination across successive steps on the HIV treatment cascade.
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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".