Screening, Detecting and Enhancing the Yield of Previously Undiagnosed Hepatitis B and C In Patients with Acute Medical Admissions to Hospital: A Pilot Project Undertaken at the Vancouver General Hospital
Bibliographic record
Abstract
BACKGROUND: Hepatitis B virus (HBV) and hepatitis C virus (HCV) represent an increasing health burden and morbidity in Canada. Viral hepatitis, specifically HCV, has high prevalence among persons born between 1945 and 1965, with 45% to 85% of infected adults asymptomatic and unaware of their infection. Screening has been shown to be cost effective in the detection and treatment of viral hepatitis. OBJECTIVE: To quantify incidence and identify undocumented HBV and HCV infection in hospitalized patients at a single centre with secondary analysis of risk factors as part of a quality improvement initiative. METHODS: A one-time antibody test was conducted in patients admitted to the acute medicine and gastroenterology services. RESULTS: Over a 12-week period, hospital screening for HBV and HCV was performed in 37.3% of 995 admitted patients. There was identification of 15 previously undiagnosed cases of HCV (4%) and 36 undocumented cases of occult (ie, antihepatitis B core antigen seropositive) or active (ie, hepatitis B surface antigen seropositive) HBV (9.7%). Among patients with positive screens, 60% of seropositive HCV patients had no identifiable risk factors. CONCLUSIONS: The prevalence of HBV and HCV infection among hospitalized patients in Vancouver was higher than that of the general population. Risk factors for contraction are often not identified. These results can be used as part of an ongoing discussion regarding a 'seek and treat' approach to the detection and treatment of chronic blood-borne viral illnesses.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".