Comorbid symptoms of emotional distress in adult survivors of childhood cancer
Bibliographic record
Abstract
BACKGROUND: Childhood cancer survivors are at risk for emotional distress symptoms, but symptom comorbidity has not been previously examined. This study examined distress profiles for adult survivors of childhood cancer diagnosed between 1970 and 1999. METHODS: Self-reported depression, anxiety, and somatization symptoms from Brief Symptom Inventory 18 were examined in survivors (n = 16,079) and siblings (n = 3085) from the Childhood Cancer Survivor Study. A latent profile analysis identified clusters of survivors with individual and comorbid symptoms. Disease, treatment, and demographic predictors of distress comorbidity patterns were examined with multinomial logistic regressions. RESULTS: Four clinically relevant profiles were identified: low distress on all subscales (asymptomatic, 62%), high distress on all subscales (comorbid distress, 11%), elevated somatization (somatic symptoms, 13%), and elevated depression and anxiety (affective distress, 14%). Compared with siblings, fewer survivors were asymptomatic (62% vs 74%, P < .0001), and more had comorbid distress (11% vs 5%, P < .0001). Survivors of leukemia (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.12-1.61), central nervous system tumors (OR, 1.30; 95% CI, 1.05-1.61), and sarcoma (OR, 1.26; 95% CI, 1.01-1.57) had a greater risk of comorbid distress than survivors of solid tumors. Psychoactive medications were associated with comorbid distress (P < .0001), and this suggested that this group was refractory to traditional medical management. Comorbid distress was associated with poor perceived health (OR, 31.7; 95% CI, 23.1-43.3), headaches (OR, 3.2; 95% CI, 2.8-3.7), and bodily pain (OR, 4.0; 95% CI, 3.2-5.0). CONCLUSIONS: A significant proportion of survivors are at risk for comorbid distress, which may require extensive treatment approaches beyond those used for individual symptoms. Cancer 2016;122:3215-24. © 2016 American Cancer Society.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".