Characteristics and survival of HIV‐infected patients not screened for hepatitis C virus infection in a hospital‐based cohort
Bibliographic record
Abstract
Summary. The rate of human immunodeficiency virus (HIV) disease progression or death of individuals coinfected with hepatitis C virus (HCV) is conflicting. The complete‐case analysis systematically used, excludes patients unscreened for HCV. Our objective was to assess if rate of survival differed between HIV‐infected patients screened and unscreened for HCV in a hospital‐based prospective cohort study. Patients were enrolled in the Lyon section of the French Hospital Database on HIV between 1 July 1992 and 31 May 2005. A multivariate Cox regression model was used to analyse the association of HCV screening with survival. Of 3244 patients, 299 (9.2%) were not screened for HCV. The populations screened and unscreened differed by the proportion of acquired immune deficiency syndrome at baseline, presumed route of infection, CD4 cell count category at baseline, mean duration of follow‐up, mean number of visits per year, type of antiretroviral therapy and survival. The rate of progression to death was higher for non‐HCV‐screened vs HCV‐screened patients: the incidence rate among HCV‐screened patients was 22.9/1000 patient‐years; the incidence rate among HCV‐unscreened patients was 52.4/1000 patient‐years. The adjusted hazards ratio of death was 2.48 [95% confidence interval (1.83–3.35); P < 0.001] for patients with unknown HCV status compared with others. In conclusion, unscreened or unknown HCV status was associated with an increased risk of death in our hospital cohort. Important prognostic factors are related to, or confounded by the practice of HCV screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".