Relationship between the cumulative burden (CB) of chronic health conditions (CHC) and health-related quality of life (HRQoL) among childhood cancer survivors (CCS): The St. Jude Lifetime (SJLIFE) cohort.
Bibliographic record
Abstract
10560 Background: Adult CCS experience an excess burden of CHC. The association between disease burden (estimated using CB) and HRQoL has not been extensively assessed. Methods: 2878 CCS (mean [range] age 32.1 [18.3-66.2] years; time from diagnosis 25.0 [10.2-51.0] years) were enrolled in SJLIFE (eligibility: survived >10 years and >18 years of age) and clinically evaluated for 168 graded CHC using the St. Jude modified Common Terminology Criteria for Adverse Events. HRQoL was assessed using the Short Form 36 survey and categorized into Low (< -0.5 SDs), Average (-0.5 to 0.5 SDs), and High (> 0.5 SDs) subgroups from the Physical and Mental Component Summary (PCS, MCS) and Vitality Scale using cohort age- and sex-specific values. CB (average number of grade 3-4 [severe/life-threatening] CHC/survivor) for each CHC was calculated and summed for each HRQoL subgroup. Results: Survivors with low PCS had, on average, more CHC CB compared to those with High and Average PCS. Higher CHC CB was also associated with poorer Vitality and MCS, but the differences in effect size were smaller than PCS. When CB for each of the 3 HRQoL scores were compared by subgroups across 12 organ systems and subsequent neoplasms, CB at age 50 differed significantly (p<0.05) across PCS, MCS, and Vitality in 9, 3 and 7 of the 13 systems, respectively. Conclusions: Survivors with lower HRQoL scores have more CHC, but the patterns of this association vary in PCS, MCS and Vitality by CHC organ systems, suggesting adult CCS adjust better to certain types of CHC than others. Future research will focus on CHC with greatest impact on functioning. [Table: see text]
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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 | 0.001 |
| 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".