Risk factors for the development of depression in patients with hepatitis C taking interferon-α
Bibliographic record
Abstract
Risk factors for the development of depression in patients with hepatitis C taking interferon-α Kimberley J Smith¹, Suzanne Norris², Cliona O'Farrelly³, O'Mara SM¹¹Trinity College Institute of Neuroscience, ²Hepatology Centre, St James's Hospital, Dublin, Ireland; ³Department of Biochemistry and Immunology, Trinity College Dublin, Dublin, IrelandAbstract: Interferon-α, currently used for the treatment of hepatitis C, is associated with a substantially elevated risk of depression. However, not everyone who takes this drug becomes depressed, so it is important to understand what particular factors may make some individuals more ‘at risk' of developing depression than others. Currently there is no consensus as to why interferon-induced depression occurs and the range of putative risk factors is wide and diverse. The identification of risk factors prior to treatment may allow identification of patients who will become depressed on interferon, allowing the possibility of improved treatment support and rates of treatment adherence. Here, we consolidate and review the literature on risk factors, and we discuss the potential confounds within the research examined in order to better isolate the risk factors that may be important in the development of depression in these patients and which might help predict patients likely to become depressed on treatment. We suggest that interactions between psychobehavioral, genetic, and biological risk factors are of particular importance in the occurrence of depression in patients with hepatitis C taking interferon-α.Keywords: interferon-α, risk factors, depression, hepatitis C
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".