Bibliographic record
Abstract
Background: Hepatitis C virus infection is associated with reduced quality of life. One way to measure the quality of life of hepatitis C patients is using health utilities. Health utilities provide insight into not only the state of a patient’s health but also the strength of preference for that health state. This is a meaningful metric that can be used to measure individual and population-level health as well as conduct cost-utility analysis. Methods: We conducted three studies on health utilities in hepatitis C patients: 1) a systematic review and meta-analysis; 2) a clinical study measuring hepatitis C patients’ health utilities in hospital and community health centre settings; and 3) a population-level health utility study using linked survey and healthcare administrative data. Results: The results of these three studies support the observation that hepatitis C is associated with a reduced quality of life. The meta-analysis found that hepatitis C virus infection is associated with impaired health utility. We observed that experimental study designs yield higher health utilities than observational study designs—an effect not previously documented. We also concluded that more research is needed in socioeconomically marginalized hepatitis C patients. The clinical study attempted to address this gap by recruiting marginalized hepatitis C patients from a community-based program. This study found that marginalized patients attending the community-based program had lower health utilities than patients recruited in hospital settings, where clinical research is most commonly conducted. Lastly, the study using administrative data found that the trends observed in the first two studies held true for a larger population-based sample from across Ontario: those with hepatitis C had a reduced quality of life as measured by health utilities, and socioeconomically marginalized individuals with hepatitis C had even lower health utilities. Conclusion: These three studies extend our understanding of health utilities and quality of life in hepatitis C patients, particularly in the context of marginalized populations who face a disproportionate burden. The utility estimates generated by these studies can be incorporated into cost-utility analyses of hepatitis C programs, including cost-utility analyses assessing different models of care for marginalized hepatitis C patients.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".