Suicidal ideation in patients with cancer: A systematic review of prevalence, risk factors, intervention and assessment
Bibliographic record
Abstract
OBJECTIVES: Suicidal ideation (SI) underlies risk of death by suicide. It is well established that patients with cancer are at increased risk of death by suicide. Therefore, understanding SI in patients with cancer is critically important. The goal of this systematic review was to investigate the prevalence, risk factors, intervention, and assessment of SI in patients with cancer. METHODS: This systematic review was registered with the PROSPERO database (CRD42018115405) and was guided by the PRISMA statement. We searched Medline, PsycInfo, Embase, CINAHL, the Cochrane Database of Systematic Reviews, and Cochrane Central. Two reviewers independently screened abstracts and assessed for quality assurance using a revised Newcastle-Ottawa Scale. RESULTS: We identified 439 studies to screen for eligibility. Eligible studies included adults with cancer diagnoses and listed SI as an outcome. Ultimately, 44 studies were included in the analyses. Prevalence of SI ranged greatly from 0.7% to 46.3%. Single items drawn from validated measures were the most frequent method of assessing SI (n = 20, 45.5%); additional methods included validated measures and psychological interviews. Commonly identified risk factors for SI included age, sex, and disease/treatment-related characteristics, as well as psychological constructs including depression, anxiety, hopelessness, existential distress, and social support. SIGNIFICANCE OF RESULTS: Assessment of SI in patients with cancer is the concern of researchers worldwide. Prevalence of SI varied with study population and was likely influenced by the method of assessment. Psychological distress consistently predicted SI. Increasing awareness of demographic, clinical, and psychological associations is critical for risk assessment and intervention development.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".