Recent trends in the management of depression in persons with cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Depression is a prevalent comorbidity in cancer that significantly increases the risk for numerous negative health outcomes. This review updates the current evidence base for management of depression in cancer, highlighting new research directions based on the inflammatory hypothesis of depression. RECENT FINDINGS: Research on pharmacotherapy and psychotherapy for depression in cancer has shown mixed efficacy partly because of methodological issues arising from the phenomenology of depression in cancer. After decades of stagnancy, more recent high-quality clinical trials are beginning to provide an evidence base to guide treatment. Inflammatory cytokine-associated depression is a subtype of depression that may have particular relevance in cancer, opening new avenues to explore therapeutic targets and biobehavioral impacts of interventions, which may improve cancer outcomes. SUMMARY: The continuum of severity in cancer-related depression is important to consider in management approaches. Choice of treatment should be personalized to the patient and their symptom profile as there is currently insufficient evidence to recommend any particular medication or psychotherapy over another. Psychological interventions should be considered first line for mild-to-moderate depression, and pharmacological treatment added for more severe depression, which can be optimally delivered within a collaborative care model. VIDEO ABSTRACT: http://links.lww.com/YCO/A62.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.002 |
| 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".