Prevalence and Predictors of Frailty in Childhood Cancer Survivors and Siblings: A Report From the Childhood Cancer Survivor Study
Bibliographic record
Abstract
PURPOSE: To estimate the prevalence of frailty among childhood cancer survivors and to determine the direct and indirect effects of treatment exposures, lifestyle factors, and severe, disabling, and life-threatening chronic condition on frailty. METHODS: Childhood cancer survivors (≥ 5 years since diagnosis), treated between 1970 and 1999 when < 21 years old (n = 10,899; mean age, 37.6 ± 9.4 years; 48% male, 86% white) and siblings were included (n = 2,097; mean age, 42.9 ± 9.4 years). Frailty was defined as ≥ 3 of the following: low lean mass, exhaustion, low energy expenditure, walking limitations, and weakness. Generalized linear models were used to evaluate direct and indirect associations between frailty and treatment exposures, sociodemographic characteristics, lifestyle factors, and chronic condition. RESULTS: , amputation, or lung surgery had increased risk for frailty. These associations were partially but not completely attenuated when sociodemographic characteristics, lifestyle factors, and chronic conditions were added to multivariable models. Cranial radiation (prevalence ratio [PR], 1.47; 95% CI, 1.20 to 1.76), pelvic radiation ≥ 34 Gy (PR, 1.46; 95% CI, 1.01 to 2.11), and lung surgery (PR, 1.75; 95% CI, 1.28 to 2.38) remained significant after sociodemographic, lifestyle, and chronic conditions were accounted for. CONCLUSION: Childhood cancer survivors reported a higher prevalence of frailty compared with siblings. Radiation and lung surgery exposures were associated with increased risk for frailty. Interventions to prevent, delay onset, or remediate chronic disease and/or promote healthy lifestyle are needed to decrease the prevalence of frailty and preserve function in this at-risk population.
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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 | 0.002 |
| 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 | 0.000 |
| 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".