ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026
Psychological distress in cancer survivors: a population-based analysis and machine learning-based risk stratification.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeAs the number of cancer survivors increases, psychological distress has become an important issue. Using nationally representative data, we evaluated mental health outcomes among Korean cancer survivors compared with cancer-free controls and developed models to identify individuals at risk of psychological distress.
methodsWe analyzed data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2021. Psychological outcomes were assessed using standardized questionnaires, and a composite distress outcome was constructed. Risk stratification models were developed among cancer survivors using logistic regression and machine learning algorithms, including random forest, XGBoost, LightGBM, support vector machines, k-nearest neighbors, and naïve Bayes.
resultsA total of 88,061 participants were included, comprising 3733 cancer survivors and 84,328 cancer-free controls. Compared with cancer-free controls, cancer survivors had higher odds of depressed mood (OR 1.33; 95% CI 1.18-1.51), suicidal ideation (OR 1.14; 95% CI 1.00-1.31), suicide planning (OR 1.91; 95% CI 1.37-2.65), and mental health counseling (OR 1.36; 95% CI 1.08-1.71). Among cancer survivors, multiple models were evaluated, with logistic regression showing the highest performance (AUROC 0.689), followed by XGBoost (0.686). In logistic regression, longer working hours, depression history, activity limitation, female sex, smoking, employment, low income, and distorted body image were independently associated with distress. SHAP analysis identified activity limitation, sex, and depression history as key factors.
conclusionsCancer survivors experience increased psychological distress across multiple outcomes. Machine learning-based models may help identify individuals at higher risk of psychological distress, supporting risk-based assessment in survivorship care.
Indexed as
Identifiers
42192026What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.