Clinical and Demographic Factors Associated with Distress in Adolescent and Young Adults with Cancer
Bibliographic record
Abstract
Purpose: Distress in cancer is defined as multifactorial unpleasant experience of an emotional, psychological, social, or spiritual nature that interferes with ones' ability to cope with cancer and its symptoms and treatment. The aim of this study was to determine clinical and demographic factors associated with the presence of distress in adolescent and young adults (AYAs) with cancer. Methods: Data were collected as part of a field-test study conducted between August 2016 and November 2017 in Canada (Toronto, Edmonton, and Vancouver) to determine the reliability and validity of CDS-AYA (Cancer Distress Scales for Adolescent and Young Adults). The CDS-AYA consist of five independently functioning scales including impact of cancer, physical, emotional, cognitive, and cancer worry. Multivariate logistic regression analyses, using established CDS-AYA cut points, were performed to identify clinical and demographic factors associated with the presence of distress in AYAs of ages 15–39 years with cancer. Results: Across all scales, increased distress was associated with female gender (p < 0.05), on-treatment status (p < 0.05), and reported poor overall health (p < 0.001). For the emotional scale, distress was also associated with being of age 15–19 years (p = 0.01). The greatest effect size for all scales was associated with treatment status [exp(β) = 1.78–4.6], except for the cognitive scale where gender had a slightly greater effect size. Conclusion: Factors associated with distress in AYA patients with cancer were similar across five CDS-AYA scales. Although it is important to screen all patients for distress, our findings reveal that patients who are female, on treatment, or who report having poorer health may be at a greater risk.
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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 | 0.005 |
| 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 | 0.001 | 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".