The effect of injecting drug use history on disease progression and death among HIV‐positive individuals initiating combination antiretroviral therapy: collaborative cohort analysis
Bibliographic record
Abstract
BACKGROUND: We examined whether determinants of disease progression and causes of death differ between injecting drug users (IDUs) and non-IDUs who initiate combination antiretroviral therapy (cART). METHODS: The ART Cohort Collaboration combines data from participating cohort studies on cART-naïve adults from cART initiation. We used Cox models to estimate hazard ratios for death and AIDS among IDUs and non-IDUs. The cumulative incidence of specific causes of death was calculated and compared using methods that allow for competing risks. RESULTS: Data on 6269 IDUs and 37 774 non-IDUs were analysed. Compared with non-IDUs, a lower proportion of IDUs initiated cART with a CD4 cell count <200 cells/μL or had a prior diagnosis of AIDS. Mortality rates were higher in IDUs than in non-IDUs (2.08 vs. 1.04 per 100 person-years, respectively; P<0.001). Lower baseline CD4 cell count, higher baseline HIV viral load, clinical AIDS at baseline, and later year of cART initiation were associated with disease progression in both groups. However, the inverse association of baseline CD4 cell count with AIDS and death appeared stronger in non-IDUs than in IDUs. The risk of death from each specific cause was higher in IDUs than non-IDUs, with particularly marked increases in risk for liver-related deaths, and those from violence and non-AIDS infection. CONCLUSION: While liver-related deaths and deaths from direct effects of substance abuse appear to explain much of the excess mortality in IDUs, they are at increased risk for many other causes of death, which may relate to suboptimal management of HIV disease in these individuals.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".