Kaposi sarcoma in people living with HIV: incidence and associated factors in a French cohort between 2010 and 2015
Bibliographic record
Abstract
OBJECTIVE: Kaposi sarcoma is still observed among people living with HIV (PLHIV) including those on ART with undetectable HIV viral load (HIV-VL). We aimed to assess Kaposi sarcoma incidence and trends between 2010 and 2015 in France and to highlight associated factors. DESIGN: Retrospective study using longitudinal data from the Dat'AIDS cohort including 44 642 PLWH. For the incidence assessment, Kaposi sarcoma cases occurring within 30 days of cohort enrollment were excluded. METHODS: Demographic, immunological, and therapeutic characteristics collected at time of Kaposi sarcoma diagnosis or at last visit for patients without Kaposi sarcoma. RESULTS: Among 180 216.4 person-years, Kaposi sarcoma incidence was 76 (95% CI 64.3-89.9)/10 person-years. Multivariate analysis (Poisson regression) revealed the positive association with male sex, MSM transmission route, lower CD4 T-cell count, higher CD8 T-cell count, not to be on ART, whereas HIV follow-up time, duration with an HIV-VL 50 copies/ml or less were negatively associated with Kaposi sarcoma. According to the different models tested, HIV-VL, CD4 : CD8 ratio and nadir CD4 cell count were associated with Kaposi sarcoma. Moreover, stratified analysis showed that patients with a CD4 : CD8 ratio 0.5 or less or a CD8 T-cell count greater than 1000 cells/μl were at higher risk of Kaposi sarcoma regardless of the CD4 T-cell count. CONCLUSION: This study showed that in a resource-rich country setting with high ART coverage, Kaposi sarcoma still occurred among PLWH. CD8 hyperlymphocytosis and CD4 : CD8 ratio should be now considered as two useful markers to better identify patients at increased Kaposi sarcoma risk, including those with a CD4 T-cell count greater than 500 cells/μl.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".