Clinical outcomes of <scp>HIV</scp>‐infected patients with <scp>K</scp>aposi's sarcoma receiving nonnucleoside reverse transcriptase inhibitor‐based antiretroviral therapy in <scp>U</scp>ganda
Bibliographic record
Abstract
BACKGROUND: Clinical outcomes for patients with Kaposi's sarcoma (KS) using nonnucleoside reverse transcriptase inhibitor (NNRTI)-based highly active antiretroviral therapy (HAART) in resource-limited settings have not previously been described. METHODS: We evaluated HIV-infected patients aged ≥ 18 years, who initiated HAART in the Home-Based AIDS Care (HBAC) project in Tororo, Uganda, between May 2003 and February 2008 and were diagnosed with KS at baseline or during follow-up. We examined independent risk factors for having either prevalent or incident KS and risk factors for death among patients with KS. RESULTS: Of 1121 study subjects, 17 (1.5%) were diagnosed with prevalent KS and 18 (1.6%) with incident KS over a median of 56.1 months of follow-up. KS was associated with male sex [adjusted odds ratio (AOR) 2.41; 95% confidence interval (CI) 1.20-4.86] and baseline CD4 cell count < 50 cells/μL (AOR 3.25; 95% CI 1.03-10.3). Eleven (65%) of 17 patients with prevalent KS and 13 (72%) of 18 patients with incident KS experienced complete regression (P = 0.137). Eighteen (64%) of 28 patients who remained on NNRTI-based HAART experienced regression of their KS and six (86%) of seven patients who were switched to protease inhibitor-containing HAART regimens had regression of their KS (P = 0.23). Mortality among those with KS was significantly associated with visceral disease (hazard ratio 19.22; 95% CI 2.42-152). CONCLUSION: Prevalent or incident KS was associated with 30% mortality. The resolution of KS lesions among individuals who initiated HAART with NNRTI-based regimens was similar to that found in studies using only protease inhibitor-based HAART.
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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 | 0.000 | 0.000 |
| 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".