Immune recovery after antiretroviral therapy initiation: a challenge for people living with HIV in Brazil
Bibliographic record
Abstract
Immune recovery reflects health conditions. Our goal was to estimate the time it takes to achieve immune recovery and its associated factors, in people living with HIV (PLHIV), after antiretroviral therapy (ART) initiation. A historical cohort study was performed among PLHIV (> 18 years-old) in Minas Gerais State, Brazil, using data from healthcare databases. Patients initiating ART between 2009-2018, with T-CD4+ lymphocytes and viral load recorded before and after antiretroviral therapy were included. The outcome is achievement of immune recovery, defined as the first T-CD4+ > 500 cells/µL after ART initiation. Explanatory variables were age, gender, place of residence, year of ART initiation, baseline viral load and T-CD4+, viral load status, and adherence to ART at follow-up. Descriptive analysis, cumulative, and person-time incidences of immune recovery were estimated. Median-time to immune recovery was estimated using Kaplan-Meier method. Factors associated with immune recovery were assessed by Cox regression. Among 26,430 PLHIV, 8,014 (30%) were eligible. Most were male (67%), mean age 38.7 years, resided in non-central region, median-baseline T-CD4+ = 228 cells/µL (< 200 cells/µL = 43%) and viral load median-baseline = 4.7 log10 copies/mL (detectable viral load = 99%). Follow-up time = 15,872 person-years. Cumulative and incidence rate were 58% (95%CI: 57-58) (n = 4,678) and 29.47 cases/100 person-years, respectively. Median-time to immune recovery was of 22.8 months (95%CI: 21.9-24.0). Women living with HIV, younger than 38 years of age, with T-CD4+ baseline > 200 cells/µL, detectable viral load (baseline), antiretroviral therapy-adherence and undetectable viral load (follow-up) were independently associated with immune recovery. Time to immune recovery remains long and depends on early treatment and antiretroviral therapy-adherence.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".