Depression and Suicidal Ideation Among Patients With Cancer in the United States: A Population-Based Study
Bibliographic record
Abstract
PURPOSE: To evaluate the association between cancer diagnosis and depression and suicidal ideation in a population-based cohort in the United States. METHODS: This was a cross-sectional study based on the National Health and Nutrition Examination Survey (NHANES) conducted for the years 2005 to 2016. Depression was assessed using a validated tool (Patient Health Questionnaire-9, and suicidal ideation was assessed by item number 9 of this tool. Propensity score matching was conducted to match survey respondents with cancer versus those without cancer. Multivariable logistic regression analysis was then conducted to evaluate factors associated with higher probability of depression and suicide among the whole postpropensity cohort. RESULTS: A total of 32,178 survey respondents were eligible and included in the study. These included 3,043 respondents with cancer and 29,675 respondents without cancer. Within the postpropensity cohort, a cancer diagnosis was not associated with a higher probability of depressive disorders (odds ratio, 0.937; 95% CI, 0.819 to 1.073), whereas it was associated with a higher probability of suicidal ideation (for respondents without cancer v those with cancer: odds ratio, 0.695; 95% CI, 0.517 to 0.935). CONCLUSION: Cancer diagnosis is associated with a higher probability of suicidal ideation. Screening for suicidal ideation should be part of the assessment of patients with cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".