Defining the genital immune correlates of protection against HIV acquisition: co-infections and other potential confounders
Bibliographic record
Abstract
The sexual transmission of HIV is very inefficient, presumably because mucosal immune defences prevent infection after most exposures. Since numerous genital immune factors have antiviral effects in vitro, their elucidation might greatly inform the microbicide and HIV prevention fields, particularly in the context of HIV-exposed but persistently seronegative (ESN) individuals. However, several important confounders must be considered in such research. First, sound epidemiological criteria are needed to define individuals as ESN. Then, since high-risk sexual activity is commonly one of these criteria, its potential impact on genital immunology must be carefully considered, both the direct effects of sex and the secondary immune effects of genital co-infections. This means that it may be very difficult to determine whether differences in genital immunology between ESN and control groups are responsible for HIV protection, or are a consequence of high-risk sexual activity. To overcome this confounding, the demographics and epidemiology of ESN cohorts must be described very carefully, thorough co-infection diagnostics must be performed and, if possible, prospective studies with an endpoint of HIV acquisition should be performed to define the direction of causality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".