Current and Past Immunodeficiency Are Associated With Higher Hospitalization Rates Among Persons on Virologically Suppressive Antiretroviral Therapy for up to 11 Years
Bibliographic record
Abstract
BACKGROUND: Persons with human immunodeficiency virus (PWH) with persistently low CD4 counts despite efficacious antiretroviral therapy could have higher hospitalization risk. METHODS: In 6 US and Canadian clinical cohorts, PWH with virologic suppression for ≥1 year in 2005-2015 were followed until virologic failure, loss to follow-up, death, or study end. Stratified by early (years 2-5) and long-term (years 6-11) suppression and lowest presuppression CD4 count <200 and ≥200 cells/µL, Poisson regression models estimated hospitalization incidence rate ratios (aIRRs) comparing patients by time-updated CD4 count category, adjusted for cohort, age, gender, calendar year, suppression duration, and lowest presuppression CD4 count. RESULTS: The 6997 included patients (19 980 person-years) were 81% cisgender men and 40% white. Among patients with lowest presuppression CD4 count <200 cells/μL (44%), patients with current CD4 count 200-350 vs >500 cells/μL had aIRRs of 1.44 during early suppression (95% confidence interval [CI], 1.01-2.06), and 1.67 (95% CI, 1.03-2.72) during long-term suppression. Among patients with lowest presuppression CD4 count ≥200 (56%), patients with current CD4 351-500 vs >500 cells/μL had an aIRR of 1.22 (95% CI, .93-1.60) during early suppression and 2.09 (95% CI, 1.18-3.70) during long-term suppression. CONCLUSIONS: Virologically suppressed patients with lower CD4 counts experienced higher hospitalization rates and could potentially benefit from targeted clinical management strategies.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".