Psychological morbidity associated with prostate cancer: Rates and predictors of depression in the RADICAL PC study
Bibliographic record
Abstract
INTRODUCTION: Across all cancer sites and stages, prostate cancer has one of the greatest median five-year survival rates, highlighting the important focus on survivorship issues following diagnosis and treatment. In the current study, we sought to evaluate the prevalence and predictors of depression in a large, multicenter, contemporary, prospectively collected sample of men with prostate cancer. METHODS: Data from the current study were drawn from the baseline visit of men enrolled in the RADICAL PC study. Men with a new diagnosis of prostate cancer or patients initiating androgen deprivation therapy for prostate cancer for the first time were recruited. Depressive symptoms were evaluated using the nine-item version of the Patient Health Questionnaire (PHQ-9). To evaluate factors associated with depression, a multivariable logistic regression model was constructed, including biological, psychological, and social predictor variables. RESULTS: Data from 2445 patients were analyzed. Of these, 201 (8.2%) endorsed clinically significant depression. Younger age (odds ratio [OR] 1.38, 95% confidence interval [CI] 1.16-1.60 per 10-year decrease), being a current smoker (OR 2.77, 95% CI 1.66-4.58), former alcohol use (OR 2.63, 95% CI 1.33-5.20), poorer performance status (OR 5.01, 95% CI 3.49-7.20), having a pre-existing clinical diagnosis of depression or anxiety (OR 3.64, 95% CI 2.42-5.48), and having high-risk prostate cancer (OR 1.49, 95% CI 1.05-2.12) all conferred independent risk for depression. CONCLUSIONS: Clinically significant depression is common in men with prostate cancer. Depression risk is associated with a host of biopsychosocial variables. Clinicians should be vigilant to screen for depression in those patients with poor social determinants of health, concomitant disability, and advanced disease.
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 | 0.002 |
| 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 | 0.001 |
| 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".