Pain and Fear of Cancer Recurrence in Survivors of Childhood Cancer
Bibliographic record
Abstract
OBJECTIVES: Theoretical models suggest that anxiety, pain intensity, and pain catastrophizing are implicated in a cycle that leads to heightened fear of cancer recurrence (FCR). However, these relationships have not been empirically examined. The objective of this study was to examine the relationships between anxiety symptoms, pain intensity, pain catastrophizing, and FCR in childhood cancer survivors and their parents and to examine whether pain catastrophizing predicts increased FCR beyond anxiety symptoms and pain intensity. METHODS: The participants were 54 survivors of various childhood cancers (Mage=13.1 y, range=8.4 to 17.9 y, 50% female) and their parents (94% mothers). Children reported on their pain intensity in the past 7 days. Children and parents separately completed measures of anxiety symptoms, pain catastrophizing, and FCR. RESULTS: Higher anxiety symptoms were associated with increased pain intensity, pain catastrophizing, and FCR in childhood cancer survivors. Higher anxiety symptoms and pain catastrophizing, but not child pain intensity, were associated with FCR in parents. Hierarchical linear regression models revealed that pain catastrophizing explained unique variance in both parent (ΔR2=0.11, P<0.01) and child (ΔR2=0.07, P<0.05) FCR over and above the effects of their own anxiety symptoms and child pain. DISCUSSION: The results of this study provides novel data on the association between pain and FCR and suggests that a catastrophic style of thinking about pain is more closely related to heightened FCR than one's anxiety symptoms or the sensory pain experience in both childhood cancer survivors and their parents. Pain catastrophizing may be a novel intervention target for survivors and parents struggling with fears of recurrence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".