Symptom burden, healthcare utilization, and risky behaviors in survivors of the childhood cancer survivor study (CCSS): an observation cohort study
Bibliographic record
Abstract
Background: Childhood cancer survivors face physical, psychological, and neurological symptoms that contribute to risky health behaviors and increased healthcare utilization. Traditional survivorship care models overlook risk associated with this symptom burden. The current study examined symptoms phenotypes to identify high-risk groups. Methods: Five-year survivors (N = 17,231; Mean [standard deviation] age = 27.4 [5.98]; 80% non-Hispanic White; 48% female) from the Childhood Cancer Survivor Study (NCT01120353) self-reported symptoms and risky behavior at baseline and first follow-up (original cohort data collection: baseline 1994-1998 and follow-up 2002-2004; expansion cohort: baseline 2008-2010 and follow-up 2014-2016). Medical records were extracted through chart review. Chronic health conditions (CHCs) were graded according to common terminology criteria for adverse events criteria. Latent class analysis derived symptom phenotypes. Findings: Five phenotypes emerged: 1) Low Burden (63.1%); 2) Cardio-Pulmonary-Pain (5.3%) 3); Neurologic-Pain (10.6%); 4) Psychological Distress-Pain (13.3%); 5) Global burden (7.7%). Compared to survivors with Low Burden, those in other symptom phenotypes were older, female, had lower education, no health insurance, smoked cigarettes, were physically inactive, and had ≥ grade 3 CHC (all ps < 0.05). Survivors in symptom phenotypes were at-risk for future emergency room use (all ps < 0.05). Risk for future physical inactivity was higher in Cardio-Pulmonary-Pain (OR = 1.19, CI = 1.09, 1.31), Global (OR = 1.12, CI = 1.02, 1.22), and Neurologic-Pain (OR = 1.18, CI = 1.10, 1.27) phenotypes. Cigarette use was higher in Cardio-Pulmonary-Pain (OR = 1.62, CI = 1.08, 2.42) and (Global OR = 1.65, CI = 1.17, 2.31) phenotypes. Interpretation: Symptom phenotyping identified groups at-risk for future risky health behaviors, which was not explained alone by diagnosis or CHCs. Integrating symptom assessments may guide interventions to improve health outcomes. Funding: The work was supported by the National Cancer Institute (U24 CA055727, PI: GT Armstrong). Support to St. Jude Children's Research Hospital was also provided by the National Cancer Institute Cancer Center Support grant (P30 CA021765, PI: CWM Roberts) and by the American Lebanese Syrian Associated Charities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".