Impact of Risk Factors for Specific Causes of Death in the First and Subsequent Years of Antiretroviral Therapy Among HIV-Infected Patients
Bibliographic record
Abstract
BACKGROUND: Patterns of cause-specific mortality in individuals infected with human immunodeficiency virus type 1 (HIV-1) are changing dramatically in the era of antiretroviral therapy (ART). METHODS: Sixteen cohorts from Europe and North America contributed data on adult patients followed from the start of ART. Procedures for coding causes of death were standardized. Estimated hazard ratios (HRs) were adjusted for transmission risk group, sex, age, year of ART initiation, baseline CD4 count, viral load, and AIDS status, before and after the first year of ART. RESULTS: A total of 4237 of 65 121 (6.5%) patients died (median, 4.5 years follow-up). Rates of AIDS death decreased substantially with time since starting ART, but mortality from non-AIDS malignancy increased (rate ratio, 1.04 per year; 95% confidence interval [CI], 1.0-1.1). Higher mortality in men than women during the first year of ART was mostly due to non-AIDS malignancy and liver-related deaths. Associations with age were strongest for cardiovascular disease, heart/vascular, and malignancy deaths. Patients with presumed transmission through injection drug use had higher rates of all causes of death, particularly for liver-related causes (HRs compared with men who have sex with men: 18.1 [95% CI, 6.2-52.7] during the first year of ART and 9.1 [95% CI, 5.8-14.2] thereafter). There was a persistent role of CD4 count at baseline and at 12 months in predicting AIDS, non-AIDS infection, and non-AIDS malignancy deaths. Lack of viral suppression on ART was associated with AIDS, non-AIDS infection, and other causes of death. CONCLUSIONS: Better understanding of patterns of and risk factors for cause-specific mortality in the ART era can aid in development of appropriate care for HIV-infected individuals and inform guidelines for risk factor management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".