Identifying Longitudinal CD4:CD8 Ratio Trajectories Indicative of Chronic Renal Disease Risk among People Living with HIV: An Application of Growth Mixture Models
Bibliographic record
Abstract
The incidence of chronic kidney disease (CKD) is increasing among people living with HIV (PLWH). Routine monitoring of indicators such as CD4:CD8 ratio might improve the early detection of CKD. Our objective was to identify clinically relevant CD4:CD8 ratio trajectories indicative of CKD risk. Participants were ≥ 18 years old, initiated antiretroviral therapy between 2000 and 2016, and were followed for ≥6 months until 31 March 2017 or last contact date. Outcome was incidence of CKD. Growth mixture models (GMMs) and decay models were used to compare CD4:CD8 ratio trajectories. Following GMM, 4547 (93.5%) participants were classified in Class 1 with 5.4% developing CKD, and 316 (6.5%) participants were classified in Class 2 with 20.9% developing CKD. The final model suggested that participants in Class 2 had 8.72 times the incidence rate of developing CKD than those in Class 1. Exponential decay models indicated a significant CD4:CD8 ratio decline among Class 2 participants who developed CKD. Among those who developed CKD in Class 2, starting at 5.5 years of follow-up, the slope of their ratio trajectory curve changed significantly, and the rate of decline increased dramatically. Routine monitored CD4:CD8 ratios can be an effective strategy to identify early CKD risk among PLWH.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".