Sex, Race, and Geographic Region Influence Clinical Outcomes Following Primary HIV-1 Infection
Bibliographic record
Abstract
BACKGROUND: It is unknown whether sex and race influence clinical outcomes following primary human immunodeficiency virus type 1 (HIV-1) infection. METHODS: Data were evaluated from an observational, multicenter, primarily North American cohort of HIV-1 seroconverters. RESULTS: Of 2277 seroconverters, 5.4% were women. At enrollment, women averaged .40 log₁₀ fewer copies/mL of HIV-1 RNA (P < .001) and 66 more CD4(+) T cells/μL (P = .006) than men, controlling for age and race. Antiretroviral therapy (ART) was less likely to be initiated at any time point by nonwhite women and men compared to white men (P < .005), and by individuals from the southern United States compared to others (P = .047). Sex and race did not affect responses to ART after 6 months (P > .73). Women were 2.17-fold more likely than men to experience >1 HIV/AIDS-related event (P < .001). Nonwhite women were most likely to experience an HIV/AIDS-related event compared to all others (P = .035), after adjusting for intravenous drug use and ART. Eight years after diagnosis, >1 HIV/AIDS-related event had occurred in 78% of nonwhites and 37% of whites from the southern United States, and 24% of whites and 17% of nonwhites from other regions (P < .001). CONCLUSIONS: Despite more favorable clinical parameters initially, female HIV-1-seroconverters had worse outcomes than did male seroconverters. Elevated morbidity was associated with being nonwhite and residing in the southern United States.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".