Exploring Cancer Worry in Adolescent and Young Adult Survivors of Childhood Cancers
Bibliographic record
Abstract
PURPOSE: Adolescent and young adult (AYA) survivors of cancers in childhood experience cancer worry, defined as concerns about cancer-related issues such as relapse and late effects of treatment. Cancer worry is an important determinant of successful transition to long-term follow-up care. The primary aim of this study was to identify patient-, cancer-, and treatment-related factors associated with cancer worry in AYA survivors. A secondary aim was to explore and understand inappropriate cancer worry (e.g., worry of developing a late effect when not at risk) in this population. METHODS: Two hundred and fifty AYA survivors, aged 15–26 years, completed a 6-item Cancer Worry Scale. Selection of factors potentially associated with cancer worry was guided by literature and expert opinion for inclusion in univariable and multivariable regression analyses. RESULTS: Female survivors reported significantly more cancer worry than males did (b=-9.4; 95% CI -14.4 to -4.5; p < 0.001). Survivors treated with the most intensive therapies reported more cancer worry compared with those who received the least intensive therapies (b=-18.5; 95% CI -31.2 to -5.9; p = 0.004). Thirty-one percent of participants had inappropriate worry regarding infertility and/or secondary malignancy. CONCLUSIONS: In AYA survivors, female sex and higher treatment intensity were associated with increased cancer worry. Inappropriate worry was prevalent among survivors and may contribute to unnecessary distress. These findings can help identify survivors who are more likely to worry and support the development of appropriate services to reduce the effect of cancer worry on survivor well-being.
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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 | 0.003 |
| 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 | 0.000 |
| 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".