Trends in Kaposi's Sarcoma Survival Disparities in the United States: 1980 through 2004
Bibliographic record
Abstract
BACKGROUND: Kaposi's sarcoma (KS) is the most common cancer diagnosed among people with HIV in the United States. Highly active antiretroviral therapy (HAART) is an essential treatment for KS, and recent reports document the emergence of racial disparities in KS incidence and HIV-related mortality in the post-HAART era (1996 to present). The aim of this study was to examine trends in KS survival by race from the beginning of the HIV epidemic through the introduction of HAART. METHODS: Median cause-specific survival and adjusted hazard ratios for KS from 1980 to 2004 were calculated by race using Surveillance, Epidemiology, and End Results nine-area data. RESULTS: Median survival among both black and white patients was relatively constant until 1995 (average median survival, 14 and 18 months, respectively). In 1996, white patients experienced an increase in median survival to 103 months. In subsequent years, the increase in median survival was so great that white patients did not reach 50% mortality (follow-up ending December 31, 2007). Survival among black patients increased gradually until its peak in 2001 when median survival had not been reached after 83 months of follow-up. However, subsequent relative decreases to 35 months occurred in 2002 and 2004. CONCLUSIONS: The current analysis provides evidence that there have been substantial increases in KS survival among white patients in the HAART era. Black patients have also experienced some improvements but to an attenuated extent. IMPACT: Careful attention should be paid to the continuing evolution of trends in KS survival and survival disparities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".