Incidence and survival of Kaposi Sarcoma patients: a retrospective analysis using the National Cancer Database 2004-2018
Bibliographic record
Abstract
Abstract Background Kaposi Sarcoma (KS) is a relatively uncommon skin and mucosal malignancy affecting immunocompromised and HIV/AIDS patients, with a poor prognosis. Due to its low incidence in the United States, national trends in epidemiology, treatment, and mortality within the last decade have not been characterized. Objectives We analyzed KS demographics, treatments, and mortality in the United States, 2004–2018. Methods Among KS cases diagnosed 2004–2018 in the National Cancer Database, we compared demographic and clinical characteristics of HIV + and HIV- patients, men and women, time periods. We used Kaplan-Meier survival analysis to evaluate changes in mortality over time and between subgroups. Results Of 10,027 KS patients, the mean age was 47.7\(\pm\)17.9 years, and 9,063 (90.4%) were males. The number of Black men (p<0.001) and Medicaid recipients (p < 0.001) increased over the study period. Overall, 1- and 2-year survival increased by 6.4% and 8.3%, respectively, between 2004–2007 and 2016–2018 (p < 0.0001). Among HIV+ patients, 1- and 2-year survival were 14.5% and 13.7% lower, respectively, than among HIV- patients (p=0.0074). Limitations of this study include the retrospective nature; the sample lacked complete information about B symptoms, treatment efficacy, and KS subtypes. Conclusions KS incidence among Blacks and Medicaid-insured patients has increased. Overall KS survival has improved, despite poor outcomes for HIV + patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".