Biological Factors that May Contribute to Regional and Racial Disparities in HIV Prevalence
Bibliographic record
Abstract
Citation Kaul R, Cohen CR, Chege D, Yi TJ, Tharao W, McKinnon LR, Remis R, Anzala O, Kimani J. Biological factors that may contribute to regional and racial disparities in HIV prevalence. Am J Reprod Immunol 2011; 65: 317–324 Despite tremendous regional and subregional disparities in HIV prevalence around the world, epidemiology consistently demonstrates that black communities have been disproportionately affected by the pandemic. There are many reasons for this, and a narrow focus on socio‐behavioural causes may be seen as laying blame on affected communities or individuals. HIV sexual transmission is very inefficient, and a number of biological factors are critical in determining whether an unprotected sexual exposure to HIV results in productive infection. This review will focus on ways in which biology, rather than behaviour, may contribute to regional and racial differences in HIV epidemic spread. Specific areas of focus are viral factors, host genetics, and the impact of co‐infections and host immunology. Considering biological causes for these racial disparities may help to destigmatize the issue and lead to new and more effective strategies for prevention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".