Bipolar patients can safely and successfully receive interferon-based hepatitis C antiviral treatment
Bibliographic record
Abstract
AIM: Patients with bipolar disease are often not considered for hepatitis C virus (HCV) antiviral treatment and are excluded from clinical trials because of the risk of interferon-induced exacerbation of their underlying mood disorder. As this risk has not been well quantified in bipolar patients, we evaluated the safety and efficacy of HCV treatment in this population. METHODS: A retrospective analysis of HCV patients evaluated at The Ottawa Hospital between January 2000 and February 2008 (n=910) was carried out. Information on demographics, psychiatric history and treatment, baseline liver biopsy and blood work, treatment initiation, adherence, and therapeutic outcomes was collected. This was compared between bipolar patients (B), those with a history of depression (D), and those with no mental health disorders (N). RESULTS: Of 38 bipolar patients (4.2%), 16 (42.1%) initiated HCV treatment, a rate similar to that in patients with a history of depression (41.4%) and in those without psychiatric illness (32.6%). On-treatment psychiatric complications were comparable between the bipolar and depression groups (B=68.8%, D=54.8%; P=0.29) and were higher than in those without psychiatric illness (N=37.1%; P=0.01). Manic episodes were rare. [B=2 (12.5%), D=1 (0.9%), N=1 (0.7%)]. Interferon dose reduction or discontinuation rates for psychiatric complications (B=12.5%, D=7.9%, N=7.4%; P=NS), completion rates (B=50%, D=69%, N=58%), and sustained virologic response rates (genotype 1: B=33%, D=45%, N=49%) were similar between the groups. CONCLUSION: Stable bipolar patients have similar rates of on-treatment psychiatric complications as patients with a history of depression. With pharmacologic intervention and close clinical monitoring, well-selected bipolar patients can successfully complete treatment and achieve outcomes comparable to those in nonbipolar patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".