Bibliographic record
Abstract
OBJECTIVE: The prevalence of hepatitis C virus (HCV) infection in Canada is estimated to be 1% and expected to increase during the next decade. Mental illness, particularly depression, is common among HCV-infected patients and remains an obstacle to interferon-alpha (IFN-alpha) treatment. We summarize the risk factors for interferon-alpha-induced major depressive disorder (IFN-alpha-MDD) in HCV patients and the evidence for antidepressant prophylaxis and symptomatic antidepressant treatment of depression. METHODS: We searched MEDLINE, EMBASE, and CINAHL for randomized controlled or quasi-experimental trials evaluating antidepressant prophylactic and symptomatic treatment approaches for depression emerging during IFN-alpha treatment. Manual searches of references listed in review articles, case series, and anecdotal reports supplemented our literature search. RESULTS: A total of 9 trials involving prophylactic and symptomatic treatment approaches for IFN-alpha-MDD are summarized in our review. Antidepressant pretreatment is beneficial for patients with elevated baseline depressive symptoms and a preexisting history of IFN-alpha-MDD. Although limited evidence exists for several antidepressant agents, much of the evidence suggests that selective serotonin reuptake inhibitors (SSRIs) are safe and efficacious in treating depressive symptoms secondary to IFN-alpha therapy. CONCLUSION: Both antidepressant pretreatment and symptomatic treatment are viable strategies for treating IFN-alpha-MDD. Improved treatment outcomes and early identification of depression during HCV treatment can be achieved using an integrated medical and mental health treatment approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".