Bibliographic record
Abstract
Measuring HIV incidence is critical to monitoring and, in the long run, controlling HIV infection in populations at risk worldwide. The serologic testing algorithm for recent HIV seroconversion (STARHS) assay allows one, under the right conditions, to determine HIV incidence from a single specimen. This approach is both elegant and exciting, since it can yield estimates of incidence in many situations where cohort studies are not feasible. Longitudinal cohort studies follow initially uninfected persons over time but are expensive, time-consuming and subject to biases related to selective recruitment and attrition as well as intervention effect which make their interpretation problematic. White and colleagues have recently published two reports in Sexually Transmitted Infections which examine an important issue in the application of STARHS.1 2 To help realise the enormous potential of such an approach, the WHO formed an international working group following a special session of the International AIDS Conference in Toronto in 2006. This WHO Working Group on HIV Incidence Assays recently published a comprehensive review of the progress made in the last 10 years and the substantial challenges remaining.3 In addition, UNAIDS recently launched the High Commission on HIV Prevention to reinvigorate HIV prevention worldwide; the Commission has already expressed a strong interest in promoting and exploiting improvements in …
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.047 | 0.041 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".