Predictors of Antiviral Therapy in a Post-Transfusion Cohort of Hepatitis C Patients
Bibliographic record
Abstract
INTRODUCTION: In the past, antiviral therapy has been given to 15% to 30% of patients infected with hepatitis C virus (HCV). The efficacy of therapy has recently improved with the addition of ribavirin and pegylated interferon. The aim of the present study was to identify the clinical, socioeconomic and health-system predictors of antiviral treatment for HCV. METHODS: A retrospective analysis of compensation claims data of patients who acquired HCV through blood transfusions between 1986 and 1990 was performed. The patients consisted of 2456 Canadian HCV-positive individuals. The authors reviewed narrative comments from physicians, and constructed univariate and multivariate logistic regression models, using receipt of antiviral therapy with interferon or interferon/ribavirin as the primary outcome. RESULTS: Of the 2456 patients, approximately 30% appeared to be eligible, but only 16% received treatment. Univariate analyses suggested that the disease severity, age, HIV status and province of residence were associated with the likelihood of receiving treatment (P<0.01). The final, multivariable model indicated that in patients with HCV: intermediate disease severity (eg, fibrosis, P<0.0001); middle age (P<0.0001); HIV-negative status (P<0.0001); and province of residence (Quebec, P<0.0001; and Saskatchewan, P<0.0001) were independent predictors of treatment. Narrative comments of physicians emphasized the importance of age, HIV status and patient preferences in clinical decision-making. DISCUSSION: Given the efficacy and cost-effectiveness of current antiviral therapy, treatment rates of HCV patients may be suboptimal. Further work is required to understand barriers to treatment related to geography, organization of medical care, age, medical provider and patient preferences.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".