Long-term Mortality in HIV-Positive Individuals Virally Suppressed for > 3 Years With Incomplete CD4 Recovery
Bibliographic record
Abstract
Background. Some human immunodeficiency virus (HIV)-infected individuals initiating combination antiretroviral therapy (cART) with low CD4 counts achieve viral suppression but not CD4 cell recovery. We aimed to identify (1) risk factors for failure to achieve CD4 count >200 cells/mu L after 3 years of sustained viral suppression and (2) the association of the achieved CD4 count with subsequent mortality. Methods. We included treated HIV-infected adults from 2 large international HIV cohorts, who had viral suppression (<= 500 HIV type 1 RNA copies/mL) for >3 years with CD4 count <= 200 cells/mu L at start of the suppressed period. Logistic regression was used to identify risk factors for incomplete CD4 recovery (<= 200 cells/mu L) and Cox regression to identify associations with mortality. Results. Of 5550 eligible individuals, 835 (15%) did not reach a CD4 count >200 cells/mu L after 3 years of suppression. Increasing age, lower initial CD4 count, male heterosexual and injection drug use transmission, cART initiation after 1998, and longer time from initiation of cART to start of the virally suppressed period were risk factors for not achieving a CD4 count >200 cells/mu L. Individuals with CD4 <= 200 cells/mu L after 3 years of viral suppression had substantially increased mortality (adjusted hazard ratio, 2.60; 95% confidence interval, 1.86-3.61) compared with those who achieved CD4 count >200 cells/mu L. The increased mortality was seen across different patient groups and for all causes of death. Conclusions. Virally suppressed HIV-positive individuals on cART who do not achieve a CD4 count >200 cells/mu L have substantially increased long-term mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".