Temporal Trends in Postseroconversion CD4 Cell Count and HIV Load: The Concerted Action on Seroconversion to AIDS and Death in Europe Collaboration, 1985–2002
Bibliographic record
Abstract
OBJECTIVE: To determine whether early postseroconversion CD4 cell counts and human immunodeficiency virus (HIV) loads have changed over time. METHODS: Our analysis was based on 22 cohorts of people with known dates of seroconversion from Europe, Australia, and Canada (Concerted Action on Seroconversion to AIDS and Death in Europe Collaboration). We focused on individuals seroconverting between 1985 and 2002 who had the first CD4 cell count (n=3687) or HIV load (n=1584) measured within 2 years of seroconversion and before antiretroviral use. Linear regression models were used to assess time trends in postseroconversion CD4 cell count and HIV load. Trends in time to key thresholds were also assessed, using survival analysis. RESULTS: The overall median initial CD4 cell count was 570 cells/ microL (interquartile range [IQR], 413-780 cells/ microL). The median initial HIV load was 35,542 copies/mL (IQR, 7600-153,050 copies/mL; on log(10) scale, 3.9-5.2 log(10) copies/mL). The postseroconversion CD4 cell count changed by an average of -6.33 cells/ microL/year (95% confidence interval [CI], -8.47 to -4.20 cells/ microL/year; P<.001), whereas an increase was observed in log(10) HIV load (+0.044 log(10) copies/mL/year; 95% CI, +0.034 to +0.053 log(10) copies/mL/year). These trends remained after adjusting for potential confounders. The probability of progressing to a CD4 cell count of <500 cells/ microL by 24 months from seroconversion increased from 0.66 (95% CI, 0.63-0.69) for individuals who seroconverted before 1991 to 0.80 (95% CI, 0.75-0.84) for those who seroconverted during 1999-2002. CONCLUSION: These data suggest that, in Europe, there has been a trend of decrease in the early CD4 cell count and of increase in the early HIV load. Additional research will be necessary to determine whether similar trends exist in other geographical areas.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".