Diagnosis of Chronic Hepatitis B Pericomplication: Risk factors and Trends Over Time
Bibliographic record
Abstract
BACKGROUND AND AIMS: Hepatitis B virus (HBV) is a major cause of chronic liver disease, which can progress to cirrhosis, hepatocellular carcinoma, and death. A timely diagnosis allows for antiviral treatment, which can prevent liver-related complications. Conversely, a late diagnosis signals a missed opportunity for earlier care and treatment. Our objective was to measure the proportion of chronic HBV diagnoses that are made within 6 months of presentation with a liver disease-related complication and examine associated factors and trends over time. APPROACH AND RESULTS: We used provincial laboratory data to identify patients with chronic HBV diagnosed from 2003 to 2014. We measured the proportion who experienced a liver disease complication (decompensated cirrhosis, hepatocellular carcinoma, or liver transplant) within ±6 months of their HBV diagnosis date. A multivariable logistic regression model was used to identify factors associated with HBV diagnosis pericomplication. Of 18,434 patients with chronic HBV, 1,279 (6.9%) developed an HBV-related complication during the follow-up period. Among these, 570 (44.6%) had a first diagnosis pericomplication. HBV diagnosis pericomplication did not decrease over time and was independently associated with older age at HBV diagnosis, rural residence, alcohol use, and moderate to high levels of comorbidity. Female patients, immigrants, and those with more outpatient physician visits were less likely to have an HBV diagnosis pericomplication. CONCLUSIONS: A high proportion of patients with HBV-related complications are first diagnosed with HBV pericomplication. These signal missed opportunities for earlier detection and treatment. Our findings support expansion of HBV 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".