Impact of Depression on Mortality in Patients with Pancreatic Cancer: A Systematic Review
Bibliographic record
Abstract
The literature provides evidence of the negative impact of depression on mortality among cancer patients. Depression is also a common comorbidity in pancreatic cancer (PC). The objective of this systematic review was to provide a state-of-the-art overview of the potential role of depression in the excess mortality observed in patients with PC. Based on PRISMA guidelines, a systematic review (PROSPERO: CRD420251135451) was conducted in August 2025 using the Pubmed-Medline and Scopus database. After assessment by two readers of the 325 identified articles, 8 articles (n = 143,033) published between 1 January 2010 and 15 August 2025 investigating the specific impact of depression (diagnosed by psychiatric interviews, self-report questionnaires, or diagnostic codes) on mortality in patients with PC (diagnosed by clinical diagnosis or diagnostic codes) were included in this systematic literature review. Articles that were not research studies and were written in a language other than English/French were not included. Risk of bias was assessed using the ROBINS-I tool. A narrative synthesis of the results was performed for the potential impact of depression on mortality in patients with PC. The reported prevalence of depression in this population ranged from 7.4% to 51.8% (seven studies, n = 142,983), depending on the studies considered. Most of the included studies (seven studies, n = 141,728) consistently reported an increased risk of mortality associated with depression, regardless of cancer stage or treatment received. However, the scientific quality of these studies was generally low, with a significant risk of bias. These results suggest that better integration of depression management in the care of patients with PC could potentially improve clinical outcomes in this high-risk population.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".