Rate, Delay and Predictors of Hepatitis C Treatment in British Columbia
Bibliographic record
Abstract
BACKGROUND: The current treatment rate for chronic hepatitis C virus (HCV) infection is suboptimal despite the availability of efficacious antiviral therapy. OBJECTIVE: To determine the rate, delay and predictors of treatment in patients with chronic HCV infection. METHODS: A retrospective chart review of chronic HCV patients who were being evaluated at a tertiary hepatology centre in Vancouver, British Columbia, was performed. RESULTS: One hundred sixty-four patients with chronic HCV infection who were assessed for treatment between February 2008 and January 2013 were reviewed. Treatment was initiated in 25.6% (42 of 164). In multivariate analyses, male sex (OR 7.90 [95% CI 1.35 to 46.15]) and elevated alanine aminotransferase (ALT) level (>1.5 times the upper limit of normal) (OR 3.10 [95% CI 1.32 to 7.27]) were positive predictors of treatment, whereas active smoking (OR 0.09 [95% CI 0.02 to 0.53]) and Charlson comorbidity index (per point increase) (OR 0.47 [95% CI 0.27 to 0.83]) were negative predictors of treatment. The most common reasons for treatment deferral were no or minimal liver fibrosis in 57.7% (n=30), persistently normal ALT levels in 57.7% (n=30) and patient unreadiness in 28.8% (n=15). The most common reasons for treatment noninitiation were patient refusal in 59.1% (n=26), medical comorbidities in 36.4% (n=16), psychiatric comorbidities in 9.1% (n=4) and decompensated cirrhosis in 9.1% (n=4). There was a statistically significant difference in the median time delay from HCV diagnosis to general practitioner referral between the treated and untreated patients (66.3 versus 119.5 months, respectively [P=0.033]). The median wait time from general practitioner referral to hepatologist consult was similar between the treated and untreated patients (1.7 months versus 1.5 months, respectively [P=0.768]). Among the treated patients, the median time delay was 6.8 months from hepatologist consult to treatment initiation. CONCLUSIONS: The current treatment rate for chronic HCV infection remains suboptimal. Medical and psychiatric comorbidities represent a major obstacle to HCV treatment. Minimal hepatic fibrosis may no longer be a major reason for treatment deferral as more efficacious and tolerable antiviral therapies become available in the future. Greater educational initiatives for primary care physicians would promote early referral of patients. More nursing support would alleviate the backlog of patients awaiting treatment.
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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 | 0.001 |
| 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".