Psychological distress in prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Men diagnosed with prostate cancer (PCa) experience substantial psychological distress. Despite this, the use of screening tools in this population is limited and understudied. This study evaluates the validity of the Kessler Psychological Distress Scale (K10) as a psychological distress screening tool in men undergoing curative PCa treatment. METHODS: Participants in a PCa psychological distress prevention program (n=128) were assessed at baseline, six months, and 12 months using the K10. Exploratory (EFA) and confirmatory factor analysis (CFA) examined the scale's factor structure. Receiver operating characteristic (ROC) analyses evaluated sensitivity, specificity, and predictive values for depression and anxiety. Logistic regression assessed the impact of cutoffs on clinical psychological distress. RESULTS: EFA identified a single-factor structure (factor loadings: 0.59-0.96, variance explained: 76%). CFA confirmed model-fit (CFI=0.905; SRMR=0.042). ROC analysis demonstrated excellent predictive ability (area under the curve [AUC] 0.98, 95% confidence interval [CI] 0.95-1.0 for depression; 0.92, 95% CI 0.86-0.98 for anxiety). Youden's index suggested K10 thresholds of ≥17.5 (depression) and ≥16.5 (anxiety), although these cutoffs lacked sensitivity. With standard K10≥20 cutoffs, significant differences were observed between intervention and control groups at six months (adjusted odds ratio [aOR] 3.59, 95% CI 1.12-11.51, p=0.031) and 12 months (aOR 4.41, 95% CI 1.35-4.41, p=0.014), consistent with prior findings. CONCLUSIONS: The K10 is valid and reliable for this population, demonstrating excellent internal consistency; however, lower cutoffs (K10≥16.5, K10≥17.5) may reduce sensitivity. The standard K10≥20 threshold remains preferable for detecting distress and evaluating intervention effects in men with PCa.
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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".