Peri‐complication diagnosis of hepatitis C infection: Risk factors and trends over time
Bibliographic record
Abstract
BACKGROUND & AIMS: Hepatitis C virus (HCV) is a common and treatable cause of cirrhosis and its complications, yet many chronically infected individuals remain undiagnosed until a late stage. We sought to identify the frequency of and risk factors for HCV diagnosis peri-complication, that is within six months of an advanced liver disease complication. METHODS: This was a retrospective cohort study of Ontario residents diagnosed with chronic HCV infection between 2003 and 2014. HCV diagnosis peri-complication was defined as the occurrence of decompensated cirrhosis, hepatocellular carcinoma or liver transplant within ±6 months of HCV diagnosis. Multivariable logistic regression was used to identify risk factors for peri-complication diagnosis among all those diagnosed with HCV infection. RESULTS: Our cohort included 39,515 patients with chronic HCV infection, of whom 4.2% (n = 1645) were diagnosed peri-complication; these represented 31.6% of the 5,202 patients who developed complications in the follow-up period. Peri-complication diagnosis became more common over the study period and was associated with increasing age among baby boomers, alcohol use, diabetes mellitus, chronic HBV co-infection and moderate to high levels of morbidity. Female sex, immigrant status, having more previous outpatient physician visits, a previous emergency department visit, a history of drug use or mental health visits were associated with reduced risk of peri-complication diagnosis. CONCLUSIONS: Over a quarter of HCV-infected patients with complications were diagnosed peri-complication. This problem increased over time, suggesting a need to further expand HCV screening.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".