Bibliographic record
Abstract
"No virus, no transmission." Studies have repeatedly shown that viral load (the quantity of virus present in blood and sexual secretions) is the strongest predictor of HIV transmission during unprotected sex or transmission from infected mother to child. Effective treatment lowers viral load to undetectable levels. If one could identify and treat all HIV-infected people immediately after infection, the HIV/AIDS epidemic would eventually disappear.Such a radical solution is currently unrealistic. In reality, not all people get tested, especially when they fear stigma and discrimination. Thus, not all HIV-infected individuals are known. Of those HIV-positive individuals for whom the diagnosis is known, not all of them have access to therapy, agree to be treated, or are taking therapy effectively. Some on effective treatment will stop, and in others, the development of resistance will lead to treatment failure. Furthermore, resources are limited: should we provide drugs to asymptomatic HIV-infected individuals without indication for treatment according to guidelines in order to prevent HIV transmission at the risk of diverting funding from sick patients in urgent need? In fact, the preventive potential of anti-HIV drugs is unknown. Modellers have tried to fill the gap, but models differ depending on assumptions that are strongly debated. Further, indications for antiretroviral treatments expand; in places like Vancouver and San Francisco, the majority of HIV-positive individuals are now under treatment, and the incidence of new HIV infections has recently fallen. However, correlation does not necessarily imply causation. Finally, studies in couples where one partner is HIV-infected also appear to show that treatment reduces the risk of transmission.More definite studies, where a number of communities are randomized to either receive the "test-and-treat" approach or continue as before, are now in evaluation by funding agencies. Repeated waves of testing would precisely measure the incidence of HIV infection. Such trials face formidable logistical, practical and ethical obstacles. However, without definitive data, the intuitive appeal of "test-and-treat" is unlikely to translate into action on a global scale. In the meantime, based on the available evidence, we must strive to provide treatment to all those in medical need under the current medical guidelines. This will lead to a decrease in HIV transmission while "test-and-treat" is fully explored in prospective clinical trials.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.218 | 0.071 |
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".