Estimating variations between health care centres in the uptake of Hepatitis C Virus (HCV) treatment in HIV-HCV co-infected patients
Bibliographic record
Abstract
The effect of Health Care Centres on uptake of Hepatitis C Virus (HCV) treatment in HIV-HCV co-infected patientsBackgroundThe purpose of the study was to investigate the effect of health care centres on HCV treatment uptake after adjusting for case-mix variables. Methods Using data from the Canadian Co-infection Cohort, we modelled time to HCV treatment uptake using a Bayesian survival analysis model with random intercepts for each of the 16 cohort centres. To take into account variability in patient populations served at each centre (case-mix), models were adjusted for age, gender, ethnicity, HCV genotype; and at cohort enrolment, duration of HCV infection, receipt of combination antiretroviral therapy, history of psychiatric illness ,CD4 cell count, and self-reports of homelessness, use of intravenous drugs, and current use of alcohol. Variation between centres in treatment uptake was estimated and centres ranked according to their rate of starting patients on HCV treatment. Results Among 996 cohort participants, 390 were excluded (past HCV treatment n=170, spontaneous clearance n=50, contribution of only enrolment data n=160 and missing baseline CD4 cell count n=10). Of the remaining 606 participants, 122 started HCV treatment. Patients with more favourable HCV genotypes (2 or 3) were more likely to initiate treatment. Two centres more frequently initiated patients on HCV treatment while one centre did so less often. Conclusions After adjustment for case-mix, there was still appreciable variation in treatment uptake between centres. Determining the factors associated with higher HCV treatment rates at two centres may be informative for improving wider access to HCV treatment for co-infected persons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".