Fear of cancer recurrence among survivors of childhood cancer
Bibliographic record
Abstract
OBJECTIVES: Fear of cancer recurrence (FCR) has not been widely explored in survivors of childhood cancer. Yet, childhood survivors are at risk of experiencing late effects and may be especially vulnerable. The aims of the current study were to conduct a retrospective chart review to determine the prevalence and persistence of FCR among survivors of childhood cancer and to examine factors that may be related to FCR. METHODS: Survivors of childhood cancer (n = 228, mean attained age = 14.5 years [range = 4.7-21 years]; mean diagnosis age = 4.4 years [range = 0-16.5 years]; mean time off treatment = 8.7 years [range = 2.8-19.3 years]) seen in a Long-Term Survivor Clinic (LTSC) completed questionnaires at each clinic visit detailing their current health. FCR was measured with a single item. Data from questionnaires from 2011 to 2018 were analyzed retrospectively. Descriptive statistics and a random effects model were used to address study aims. RESULTS: FCR was reported in 43% (n = 98) of survivors at least once across all clinic visits. Among survivors reporting FCR at least once, 66% were diagnosed with cancer under the age of 5, and 64% were 13 years or older at their most recent follow-up. Twenty-one percent of survivors (n = 48/224) reported FCR during at least 50% of their visits. Survivors with a higher number of depressive symptoms were more likely to report FCR (OR = 1.66, P = .03). CONCLUSIONS: FCR is prevalent among survivors of childhood cancer and is related to other health concerns. Research is needed to understand who is at risk and how to.
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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 | 0.004 |
| 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 | 0.001 |
| 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".