Combating HIV/AIDS: biomedical approaches towards prevention
Bibliographic record
Abstract
For over three decades, HIV/AIDS has had a deleterious impact on public health the world over. There is still no cure for the disease although preventive strategies have evolved over the years to reduce its impact. In addition to behavioural change approaches, biomedical interventions have played a major part in reduction of HIV transmission and subsequently the burden associated with the HIV/AIDS disease. Early biomedical approaches include physical barriers such as condoms, use of clean injection equipment for intravenous drug users, blood and blood product screening. More recently, medical male circumcision and use of anti-retroviral drugs for prevention have been introduced. While these interventions have had a fundamental impact in reducing HIV incidence, the burden in many populations remains. Therefore, there is need to develop new biomedical methods to augment existing efforts. Future biomedical approaches may for instance include use of compounds that modulate the body’s immune system, such as acetylsalicylic acid, to cause resistance to HIV infection. Such approaches could be added to the HIV prevention toolkit. Keywords: HIV/AIDS, biomedical, prevention, immune quiescence Afr. J. Biomed. Res . Vol. 22 (May, 2019); 105- 114
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".