Bibliographic record
Abstract
Seropositivity for hepatitis C virus (HCV) is prevalentamong people who are HIV-positive.1 Although sev-eral authors have found that liver disease has become a leading cause of death among those infected with HIV,2–4 debate continues as to the effect of HCV infection on HIV disease progression, as measured by new AIDS-defining ill-nesses, CD4 T-cell decline or HIV-related mortality.4–9 Mortality in this population can be strongly confounded by factors such as adherence to a course of antiretroviral ther-apy (ART), illicit use of injected drugs and previous admin-istration of ART. During the 1990s, Vancouver experienced an explosive epidemic of HIV and HCV infection among the city’s 10 000 users of injected drugs;10 currently, more than 30% are coinfected with both viruses.11 Here we report on the effect of HCV serostatus on the risk of death among par-ticipants in a population-based HIV and AIDS treatment program who had received no previous ART, adjusting for adherence to ART and history of injection drug use. Speci-fically, we describe the effect of HCV serostatus on risk of death, particularly HIV-related death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.305 | 0.148 |
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".