Equitable access to HCV care in HIV-HCV co-infection can be achieved despite barriers to health care provision
Bibliographic record
Abstract
Language barrier, race, immigration status, mental health illness, substance abuse and socioeconomic status are often not considered when evaluating hepatitis C virus (HCV) sustained virological response (SVR) in human immunodeficiency virus (HIV) infection. The influence of these factors on HCV work-up, treatment initiation and SVR were assessed in an HIV-HCV coinfected population and compared to patients with HCV mono-infection. The setting was a publicly funded, urban-based, multidisciplinary viral hepatitis clinic. A clinical database was utilized to identify HIV and HCV consults between June 2000 and June 2007. Measures of access to HCV care (ie, liver biopsy and HCV antiviral initiation) and SVR as a function of the above variables were evaluated and compared between patients with HIV-HCV and HCV. HIV-HCV co-infected (n = 106) and HCV mono-infected (n = 802) patients were evaluated. HIV-HCV patients were more often white (94% versus 84%) and male (87% versus 69%). Bridging fibrosis or cirrhosis on biopsy was more frequent in HIV-HCV (37% versus 22%; P = 0.03). HIV infection itself did not influence access to biopsy (50% versus 52%) or treatment initiation (39% versus 38%). Race, language barrier, immigration status, injection drug history and socioeconomic status did not influence access to biopsy or treatment. SVR was 54% in HCV and 30% in HIV-HCV (P = 0.003). Genotype and HIV were the only evaluated variables to predict SVR. Within the context of a socialized, multidisciplinary clinic, HIV-HCV co-infected patients received similar access to HCV work-up and care as HCV mono-infected patients. SVR is diminished in HIV-HCV co-infection independent of language barrier, race, immigration status, or socioeconomic status.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".