Hepatitis C antibody and RNA tests for adults in provincial and federal correctional facilities in Ontario, Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND: Prevalence of Hepatitis C virus (HCV) has been shown to be high in correctional facilities. Testing for HCV begins with a blood test for antibodies followed by an HCV ribonucleic acid (RNA) test to establish active infection. Receiving the RNA test is a required step in the cascade of care for HCV management. PURPOSE: This thesis had two main objectives. The first was to determine an association between the facility of first testing positive for HCV antibodies and the time to receiving the RNA test. The facilities of interest were community medical facilities, provincial correctional institutions, and federal correctional institutions. The second objective sought to describe the number of duplicate antibody tests performed at Public Health Ontario Laboratories (PHOL). Once an individual tests positive for HCV antibodies, subsequent antibody tests are considered redundant. METHODS: Both objectives used a subset of PHOL data that included all HCV tests performed at PHOL from 1999-2014. A Cox’s proportional hazards model was used to determine an association between the facility of first antibody test and time to receiving the RNA test while controlling for other variables. The second objective counted the number of antibody tests performed at PHOL after an individual had demonstrated HCV positivity. RESULTS: Individuals who first tested HCV-positive in a provincial facility took longer to receive an RNA test when compared to those first tested in the community or federal prison; females were longer to receive the test when compared to males (hazard ratio [HR]=0.43; 95% confidence interval [CI]=0.37, 0.49 provincial vs. community for females in 2009-2014; HR=0.56; 95%CI=0.51, 0.62 provincial vs. community for males in 2009-2014).
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".