Evidence-Based Treatment of Depression in Patients With Cancer
Bibliographic record
Abstract
PURPOSE: Depression is a common condition in patients with cancer, although there has been a relative paucity of research on the effectiveness of treatment in this population. This review summarizes the psychosocial and pharmacologic treatment of depression in patients with cancer based on a consideration of evidence regarding etiologic factors and treatment outcomes. METHODS: A review of the evidence base for psychosocial and pharmacologic interventions for depression in patients with cancer was performed, including original studies, systematic reviews, and meta-analytic studies in the literature. RESULTS: Recent evidence from randomized controlled trials has demonstrated the efficacy of psychosocial and pharmacologic treatments to alleviate depression in patients with cancer. Further research is needed to establish their relative and combined efficacy and their role in the treatment of depression that is less severe and occurs in association with more advanced disease. First-line recommendations for the treatment of depression in patients with cancer are difficult to derive based on current evidence, because comparative studies have not been conducted to support the superiority of one treatment modality over another in this population. CONCLUSION: Both psychosocial and pharmacologic interventions have been shown to be efficacious in treating depression in cancer, but further research is needed to establish their relative and combined benefit. Future research directions include the development and evaluation of novel interventions targeted to specific biologic and psychosocial risk factors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".