Bibliographic record
Abstract
PURPOSE OF REVIEW: To explore how ethical considerations, improved diagnostics and data from clinical trials might see the lowering of some of the barriers blocking a cure for HIV infection over the next 5 years. RECENT FINDINGS: Despite the recent well publicized but eventually disappointing case reports, there remains only one successful HIV cure, the 'Berlin patient'. We will review the data suggesting that more potent agents might achieve in-vivo viral activation and explore the tantalizing phenomenon of 'posttreatment control' following treatment in primary HIV infection. We will also explore how new assays and novel interventions might move the field forward. SUMMARY: There is a need for new agents that can be safely tested to impact the viral reservoir, a more meaningful understanding of how to assay patient samples, and research into mechanisms behind how the reservoir is established and impacted by therapy. With HIV+ve individuals responding so well to antiretroviral therapy, new trials must be tested hand-in-hand with guidance from patient representatives, especially with respect to determining the acceptable risk. The road to a cure is going to be difficult, but it is vital that inevitable disappointments do not detract from the final goal, which remains worth striving for.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".