Differences in survival among adults with HIV-associated Kaposi's sarcoma during routine HIV treatment initiation in Zomba district, Malawi: a retrospective cohort analysis
Bibliographic record
Abstract
Background: The HIV epidemic is a major public health concern throughout Africa. Malawi is one of the worst affected countries in sub-Saharan Africa with a 2014 national HIV prevalence currently estimated at 10% (9.3-10.8%) by UNAIDS. Study reports, largely in the African setting comparing outcomes in HIV patients with and without Kaposi's sarcoma (KS) indicate poor prognosis and poor health outcomes amongst HIV+KS patients. Understanding the mortality risk in this patient group could help improve patient management and care. Methods: Using data for the 559 adult HIV+KS patients who started ART between 2004 and September 2011 at Zomba clinic in Malawi, we estimated relative hazard ratios for all-cause mortality by controlling for age, sex, TB status, occupation, date of starting treatment and distance to the HIV+KS clinic. Results: Patients with tuberculosis (95% CI: 1.05-4.65) and patients who started ART before 2008 (95% CI: 0.34-0.81) were at significantly greater risk of dying. A random-effects Cox model with Log-Gaussian frailties adequately described the variation in the hazard for mortality. Conclusion: The year of starting ART and TB status significantly affected survival among HIV+KS patients. A sub-population analysis of this kind can inform an efficient triage system for managing vulnerable 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".