Extending Visit Intervals for Clinically Stable Patients on Antiretroviral Therapy: Multicohort Analysis of HIV Programs in Southern Africa
Bibliographic record
Abstract
BACKGROUND: The World Health Organization recommends differentiated antiretroviral therapy (ART) delivery with longer visit intervals for clinically stable patients. We examined time trends in visit frequency and associations between criteria for clinical stability and visit frequency in ART programs in Southern Africa. METHODS: We included adults on ART from 4 programs with viral-load monitoring, 2 programs with CD4 monitoring, and 4 programs with clinical monitoring of ART. We classified patients as clinically stable based on virological (viral load <1000 copies/mL), immunological (CD4 >200 cells/µL), or clinical (no current tuberculosis) criteria. We used Poisson regression and survival models to examine associations between criteria for clinical stability and the rate of clinic visits. RESULTS: We included 180,837 patients. There were trends toward fewer visits in more recent years and with longer ART duration. In all ART programs, clinically stable patients were seen less frequently than patients receiving failing ART, but the strength of the association varied. Adjusted incidence rate ratios comparing visit rates for stable patients with patients on failing ART were 0.82 (95% confidence interval: 0.73 to 0.90) for patients classified based on the virological criterion, 0.81 (0.69 to 0.93) for patients classified based on the clinical criterion, and 0.90 (0.85 to 0.96) for patients classified based on the immunological criterion for stability. CONCLUSION: Differences in visit rates between stable patients and patients failing ART were variable and modest overall. Larger differences were seen in programs using virological criteria for clinical stability than in programs using immunological criteria. Greater access to routine viral-load monitoring may increase scale-up of differentiated ART delivery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".