Clinical Predictors and Incidence of Kaposi Sarcoma Among Males With HIV in the Treat-All Era in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Stagnating decreases in Kaposi sarcoma (KS) among men with HIV (MWH) following Treat-All policies necessitate evaluating changes in clinical drivers of KS. We examined clinical factors and their associations with KS rates among MWH in North America. METHODS: Among MWH in the North American AIDS Cohort Collaboration on Research and Design, we estimated annual KS rates (per 100 000 person-years [PY]) by viral suppression (<200 copies/mL), CD4 count (<500 vs ≥500 cells/mm3), and time since ART initiation (<1 year/naive vs ≥1 year) from 2009-2019. We quantified associations between clinical factors and KS rates using negative binomial regression, estimating incidence rate ratios (IRRs) with 95% CIs. Among MWH with KS, we estimated average annual percentage changes (AAPCs) in clinical factor distribution using joinpoint regression. RESULTS: There were 61 155 MWH (370 624 PY) contributing 262 KS diagnoses. KS decreased from 132 to 43 cases per 100 000 PY between 2009 and 2019. Viral suppression (IRR2009: .09 [95% CI: .04-.20]; IRR2019: .69 [.31-1.54]), recent/no ART initiation (IRR2009: .14 [.07-.30]; IRR2019: 1.16 [.53, 2.56]), and CD4 count ≥500 cells/mm3 (IRR2009: .13 [.05-.31]; IRR2019: .44 [.18-1.10]) were associated with reduced KS rates, attenuating over time. Unsuppressed viral load at KS diagnosis decreased by 10.6% (-15.8%, -4.8%) as did those on ART ≤1 year/naive (70%-40%; AAPC: -6.3% [-13.8%, 2.1%]). CONCLUSIONS: Our findings underscore the importance of early HIV diagnosis/treatment in reducing KS burden. Attenuating associations with HIV factors indicate that those successfully managing HIV increasingly represent KS patients. KS drivers are evolving, requiring patient/population-level monitoring.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".