Predictors of 5-Year Mortality in HIV-Infected Adults Starting Highly Active Antiretroviral Therapy in Thailand
Bibliographic record
Abstract
OBJECTIVE: To estimate the early and long-term mortalities and associated risk factors in adults receiving highly active antiretroviral therapy (HAART) in Thailand. DESIGN: A prospective observational cohort study. METHODS: Previously untreated adults starting HAART in 2002-2009 were followed-up in 43 public hospitals. Kaplan-Meier probability of survival was estimated up to 5 years of therapy. Factors associated with early (≤6 months) and long-term (>6 months) mortalities were assessed using Cox regression analyses. RESULTS: A total of 1578 adults received HAART (74% women; median age, 33 years; CD4 cell count, 124/mL), with a median follow-up of 50 months (interquartile range, 41-66). Eighty-nine patients (6%) died (37 occurred ≤6 months and 52 occurred >6 months) and 183 (12%) were lost to follow-up. Probability of survival [95% confidence interval (CI)] was 97.5% (96.7% to 98.2%) at 6 months, 96.6% (95.6% to 97.4%) at 1 year, and 93.5% (91.9% to 94.8%) at 5 years. Probability of being alive and on follow-up was 80.8% (78.5% to 82.8%) at 5 years. Early mortality was associated with anemia [adjusted hazard ratio (aHR) 3.6, 95% CI: 1.7 to 7.5] and low CD4 count (aHR 1.6, 95% CI: 1.1 to 2.2 per 50 cells decrease) at treatment initiation. Long-term mortality was associated with persistent anemia (aHR 4.9, 95% CI: 2.1 to 11.6), CD4 increase from baseline <50 cells per cubic millimeter (aHR 3.1, 95% CI: 1.6 to 5.7), and viral load >1000 copies per milliliter (aHR 2.8, 95% CI: 1.3 to 6.1) at 6 months of HAART; male gender; and calendar year of enrollment. CONCLUSIONS: Early mortality was associated with anemia and severe immunosuppression at initiation of therapy. Long-term mortality was associated with persistent anemia, CD4 count increase, and virological response at 6 months of therapy over baseline characteristics, highlighting the importance of laboratory monitoring.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".