Hepatitis C Virus Screening of High-Risk Patients in a Canadian Emergency Department
Bibliographic record
Abstract
Background. Approximately 0.7% of the Canadian population is infected with hepatitis C virus (HCV), and many individuals are unaware of their infection. Our objectives were to utilize an emergency department (ED) based point-of-care (POC) HCV screening test to describe our local population and estimate the proportion of high-risk patients in our population with undiagnosed HCV. Methods. A convenience sample of medically stable patients (≥18 years) presenting to a community ED in Calgary, AB, between April and July 2018 underwent rapid clinical screening for HCV risk factors, including history of injection drug use, healthcare in endemic countries, and other recognized criteria. High-risk patients were offered POC HCV testing. Antibody-positive patients underwent HCV-RNA testing and were linked to hepatology care. The primary outcome was the proportion of new HCV diagnoses in the high-risk population. Results. Of the 999 patients screened by survey, 247 patients (24.7%) were high-risk and eligible for testing. Of these, 123 (49.8%) were from HCV-endemic countries, while 63 (25.5%) and 31 (12.6%) patients endorsed a history of incarceration and intravenous drug use (IVDU), respectively. A total of 144 (58.3%) eligible patients agreed to testing. Of these, 6 patients were POC-positive (4.2%, CI 0.9–7.4%); all 6 had antibodies detected on confirmatory lab testing and 4 had detectable HCV-RNA viral loads in follow-up. Notably, 103 (41.7%) patients declined POC testing. Interpretation. Among 144 high-risk patients who agreed to testing, the rate of undiagnosed HCV infection was 4.2%, and the rate of undiagnosed HCV infection with detectable viral load was 2.8%. Many patients with high-risk clinical criteria refused POC testing. It is unknown if tested and untested groups have the same disease prevalence. This study shows that ED HCV screening is feasible and that a small number of previously undiagnosed patients can be identified and linked to potentially life-changing care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".