Incidence and prevalence of musculoskeletal health conditions in survivors of childhood and adolescent cancers: A report from the Swiss childhood cancer survivor study
Bibliographic record
Abstract
PURPOSE: Childhood cancer and its treatment can cause damage to the musculoskeletal system. We aimed to determine the incidence and prevalence of musculoskeletal health conditions (MSHC) in survivors, and to investigate differences by cancer-related characteristics. METHODS: We used data from the Childhood Cancer Registry and the Swiss Childhood Cancer Survivor Study, including survivors (≥5 years since diagnosis; diagnosed 1976-2015 at <20 years of age) aged ≥15 years at study. Cumulative incidence and prevalence of MSHCs (osteoporosis, limb length discrepancy, limited joint mobility, bone/joint pain, scoliosis, changes to chest/ribs and amputation) were calculated from self-reported data. RESULTS: We included 2645 survivors (53% men; median age 24 years, range 15-59 years). Prevalence and cumulative incidence of any MSHC was 21% and 26%, respectively. Incidence rate for any MSHC was 15.6/1000 person-years. Scoliosis (8%), bone/joint pain (7%) and limited joint mobility (7%) were the most prevalent MSHC. MSHC co-occurred with other health conditions in 87% of survivors. We found increased rates of MSHC in women (RR = 1.4, 95%CI: 1.2-1.7), bone tumour survivors (RR = 6.0, 95%CI: 4.5-7.9), survivors older at diagnosis (11-15 years: RR = 1.8, 95%CI: 1.5-2.3), after a relapse (RR = 1.5, 95%CI: 1.3-1.9), treatment with surgery (RR = 1.2, 95%CI: 1.0-1.5), chemotherapy (RR = 1.4, 95%CI: 1.1-1.8) or stem cell transplantation (RR = 1.6, 95%CI: 1.0-2.5), and more recent year of diagnosis (2011-2015: RR = 4.3, 95%CI: 2.8-6.8). CONCLUSION: MSHCs are prevalent in survivors, the risk is increasing in younger survivor cohorts, and MSHCs usually occur in multimorbid survivors. Strengthening of rehabilitation services and appropriate referrals are needed to mitigate the effects of the cancer and cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".