Effect of serostatus for hepatitis C virus on mortality among antiretrovirally naive HIV-positive patients
Bibliographic record
Abstract
BACKGROUND: We examined the effect of hepatitis C virus (HCV) seropositivity on risk of death among people receiving their first antiretroviral treatment (ART) for HIV infection. METHODS: In British Columbia, the HIV/ AIDS Drug Treatment Program is the only source of free ART. Patients who initiated a triple-drug ART regimen between July 31, 1996, and July 31, 2000, were included if they were ART-naive and had baseline HCV serological data. Outcomes of interest for survival analysis were deaths from natural and HIV-related causes, with a data cutoff of June 30, 2003. RESULTS: Of 1186 eligible subjects, 606 (51%) were HCV positive and 580, negative. Fewer HCV-positive people were male (78% v. 93%, p < 0.001) and had an AIDS diagnosis at baseline (11% v. 15%, p = 0.028). Their CD4 fraction was significantly higher at baseline (19% v. 16% of T lymphocytes, p < 0.001) but their absolute CD4 counts, log HIV viral load and the type of ART initiated were similar to those of HCV negative people. Of 163 deaths (from natural causes only) during the study period, 118 (19%) were in HCV positive and 45 (8%) in HCV negative patients (p < 0.001); of the 114 deaths attributed to HIV infection, these proportions were 79 (13%) versus 35 (6%; p < 0.001). After adjustment for potential confounders, HCV seropositivity remained predictive of death (adjusted hazard ratio [HR] 2.20, 95% confidence interval [CI] 1.50- 3.21, p < 0.001), especially HIV-related death (adjusted HR 1.75, 95% CI 1.13- 2.72, p = 0.012). INTERPRETATION: In this population-based HIV treatment program, we found HCV seropositivity to be an independent predictor of mortality, especially death related to HIV infection.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".