Viral Load Levels Measured at Set-Point Have Risen Over the Last Decade of the HIV Epidemic in the Netherlands
Bibliographic record
Abstract
BACKGROUND: HIV-1 RNA plasma concentration at viral set-point is associated not only with disease outcome but also with the transmission dynamics of HIV-1. We investigated whether plasma HIV-1 RNA concentration and CD4 cell count at viral set-point have changed over time in the HIV epidemic in the Netherlands. METHODOLOGY/PRINCIPAL FINDINGS: We selected 906 therapy-naïve patients with at least one plasma HIV-1 RNA concentration measured 9 to 27 months after estimated seroconversion. Changes in HIV-1 RNA and CD4 cell count at viral set-point over time were analysed using linear regression models. The ATHENA national observational cohort contributed all patients who seroconverted in or after 1996; the Amsterdam Cohort Studies (ACS) contributed seroconverters before 1996. The mean of the first HIV-1 RNA concentration measured 9-27 months after seroconversion was 4.30 log(10) copies/ml (95% CI 4.17-4.42) for seroconverters from 1984 through 1995 (n = 163); 4.27 (4.16-4.37) for seroconverters 1996-2002 (n = 232), and 4.59 (4.52-4.66) for seroconverters 2003-2007 (n = 511). Compared to patients seroconverting between 2003-2007, the adjusted mean HIV-1 RNA concentration at set-point was 0.28 log(10) copies/ml (95% CI 0.16-0.40; p<0.0001) and 0.26 (0.11-0.41; p = 0.0006) lower for those seroconverting between 1996-2002 and 1984-1995, respectively. Results were robust regardless of type of HIV-1 RNA assay, HIV-1 subtype, and interval between measurement and seroconversion. CD4 cell count at viral set-point declined over calendar time at approximately 5 cells/mm(3)/year. CONCLUSION: The HIV-1 RNA plasma concentration at viral set-point has increased over the last decade of the HIV epidemic in the Netherlands. This is accompanied by a decreasing CD4 cell count over the period 1984-2007 and may have implications for both the course of the HIV infection and the epidemic.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".