Factors Associated With Mortality of HIV-Positive Clients Receiving Methadone Maintenance Treatment in China
Bibliographic record
Abstract
BACKGROUND: Little is known about mortality of opiate users attending methadone maintenance treatment (MMT) clinics. We sought to investigate mortality and its predictors among human immunodeficiency virus (HIV)-positive MMT clients. METHODS: Records of 306 786 clients enrolled in China's MMT program from 24 March 2004 to 30 April 2011 were abstracted. Mortality rates were calculated for all HIV-positive antiretroviral treatment (ART)-naive and ART-experienced clients. Risk factors were examined using stratified proportional hazard ratios (HRs). RESULTS: The observed mortality rate for all clients was 11.8/1000 person-years (PY, 95% confidence interval [CI], 11.5-12.1) and 57.2/1000 PY (CI, 54.9-59.4) for HIV-positive clients (n = 18 193). An increase in average methadone doses to >75 mg/day was associated with a 24% reduction in mortality (HR = 0.76, CI, .70-.82), a 48% reduction for ART-naive HIV-positive clients (HR = 0.52, CI, .42-.65), and a 47% reduction for ART-experienced HIV-positive clients (HR = 0.53, CI, .46-.62). Among ART-experienced clients, initiation of ART when the CD4(+) T-cell count was >300 cells/mm(3) (HR = 0.64, CI, .43-.94) was also associated with decreased risk of death. CONCLUSIONS: We found high mortality rates among HIV-positive MMT clients, yet decreased risk of death, with earlier ART initiation and higher methadone doses. A higher daily methadone dose was associated with reduced mortality in both HIV-infected and HIV-uninfected clients, independent of ART.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".