Mechanisms of sleep disturbances in long-term cancer survivors: a childhood cancer survivor study report
Bibliographic record
Abstract
BACKGROUND: Sleep problems following childhood cancer treatment may persist into adulthood, exacerbating cancer-related late effects and putting survivors at risk for poor physical and psychosocial functioning. This study examines sleep in long-term survivors and their siblings to identify risk factors and disease correlates. METHODS: Childhood cancer survivors (≥5 years from diagnosis; n = 12 340; 51.5% female; mean [SD] age = 39.4 [9.6] years) and siblings (n = 2395; 57.1% female; age = 44.6 [10.5] years) participating in the Childhood Cancer Survivor Study completed the Pittsburgh Sleep Quality Index (PSQI). Multivariable Poisson-error generalized estimating equation compared prevalence of binary sleep outcomes between survivors and siblings and evaluated cancer history and chronic health conditions (CHC) for associations with sleep outcomes, adjusting for age (at diagnosis and current), sex, race/ethnicity, and body mass index. RESULTS: Survivors were more likely to report clinically elevated composite PSQI scores (>5; 45.1% vs 40.0%, adjusted prevalence ratio [PR] = 1.20, 95% CI = 1.13 to 1.27), symptoms of insomnia (38.8% vs 32.0%, PR = 1.26, 95% CI = 1.18 to 1.35), snoring (18.0% vs 17.4%, PR = 1.11, 95% CI = 1.01 to 1.23), and sleep medication use (13.2% vs 11.5%, PR = 1.28, 95% CI = 1.12 to 1.45) compared with siblings. Within cancer survivors, PSQI scores were similar across diagnoses. Anthracycline exposure (PR = 1.13, 95% CI = 1.03 to 1.25), abdominal radiation (PR = 1.16, 95% CI = 1.04 to 1.29), and increasing CHC burden were associated with elevated PSQI scores (PRs = 1.21-1.48). CONCLUSIONS: Among survivors, sleep problems were more closely related to CHC than diagnosis or treatment history, although longitudinal research is needed to determine the direction of this association. Frequent sleep-promoting medication use suggests interest in managing sleep problems; behavioral sleep intervention is advised for long-term management.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".