A188 REAL-LIFE MANAGEMENT OF CHRONIC HEPATITIS C VIRUS INFECTION IN CANADA: DESCRIPTION OF PATIENT PROFILE, PROGNOSTIC FACTORS AND TREATMENT STRATEGIES
Bibliographic record
Abstract
Most recent major treatment guidelines recommend considering the patient profile and baseline prognostic factors (genotype, fibrosis level, treatment history) in the management of chronic hepatitis C virus (HCV) infections (CHC). There is, therefore, a need to better characterize clinician decision-making in real-life CHC management in Canada. To describe the patient profile, prognostic factors and treatment strategies used in Canadian real-life CHC management. Multicenter chart review of CHC patients diagnosed from 2005 to 2012. Patient data were extracted for a minimum of 2 years after CHC diagnosis. 250 patients were included with a mean (SD) follow-up after diagnosis of 5.9 (2.3) years. Table1 summarizes the patient/disease characteristics at CHC diagnosis and the mode of management. A majority of patients (70.4%) received some antiviral treatment, with a lower proportion of treated G1 patients vs. non-G1 patients (64% vs. 76.7%). The most common initial treatments were IFN/RBV dual therapy (68.4%) and NS3/4-containing IFN/RBV therapy (19.3%); Among non-G1 patients, 92.4% were treated with IFN/RBV dual therapy. SVR was achieved by 51.5% of patients (G1: 44.7%; non-G1: 62.2%), 19.3% relapsed and 11.1% were non-responders with initial treatment. The majority of treated patients (72.2%) experienced AEs, the most common ones being fatigue (33%), anemia (27.3%), insomnia (21.6%), neutropenia (18.8%). Two deaths were reported. Despite the majority of HCV patients having mild fibrosis and being treated, most frequently with dual IFN/RBV therapy, SVR rates were low prior to the all-oral DAA era, highlighting the need for more efficacious treatments and suggesting that recommendations to still use IFN are not justifiable. Patient/Disease Characteristics at HCV Diagnosis *Most common ‡% of treated patients Abbvie corporation
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".