Suicide risk in hepatitis C and during interferon-alpha therapy: a review and clinical update
Bibliographic record
Abstract
Chronic hepatitis C (CHC) affects over 170 million individuals worldwide and is a growing public health concern. Despite the availability of CHC treatment, specifically interferon-α and ribavirin, treatment of CHC is limited by concerns about psychiatric side effects including risks of suicide. Although depression has been the focus of neuropsychiatric complications from interferon-alpha (IFNα), emerging evidence has contributed to our understanding of IFNα-induced suicidal ideation and attempts. Using Pubmed, we performed a literature review of all English articles published between 1989 and April 1, 2010 on suicide in untreated and IFNα-treated patients with CHC. References in all identified review articles were scanned and included in our review. A total of 17 articles were identified. Studies have suggested that the first 12 weeks of IFNα therapy are the high-risk period. Moreover, the emergence of suicidal ideation can be linked to neuropsychiatric abnormalities, specifically serotonin depletion. Pretreatment with antidepressant treatment should be reserved for high-risk groups, as this may reduce the risk of depression and thus decrease the suicide risk indirectly. Although there is a paucity of literature on suicide and suicide risk during IFNα therapy for CHC, recent studies on IFNα-induced depression have provided some potential insights into suicide in this patient population. Further research examining the effects of pharmacological and nonpharmacological interventions on suicide risk during IFNα treatment is needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".