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Record W2802388935 · doi:10.1093/sleep/zsy061.862

0863 Sleep Behaviors And Patterns In Adult Survivors Of Childhood Cancers: A Report From The Childhood Cancer Survivor Study (CCSS)

2018· article· en· W2802388935 on OpenAlexaff
Lauren C. Daniel, M Wang, Deo Kumar Srivastava, Lisa A. Schwartz, Tara M. Brinkman, Kim Edelstein, Daniel A. Mulrooney, Eric S. Zhou, Rebecca M. Howell, Todd M. Gibson, Wendy M. Leisenring, Greg Armstrong, Kevin R. Krull

Bibliographic record

VenueSLEEP · 2018
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsEpworth Sleepiness ScaleMedicineExcessive daytime sleepinessSleep disorderPopulationConfidence intervalSleep onsetPediatricsInsomniaPsychiatryPolysomnographyInternal medicineApnea

Abstract

fetched live from OpenAlex

Sleep disorders are related to emotional and physical health in the general population; research in childhood cancer survivors is limited. This study characterized sleep behaviors in survivors and examined associations among sleep, cancer diagnoses, treatment exposures, and emotional functioning. Childhood cancer survivors (≥5 years from diagnosis; n=1933; 50.8% female; mean [SD] age=35.1 [7.6] years; years since diagnosis=23.5 [4.7]) and siblings participants in the CCSS (n=380; 52.4% female; age=33.4 [8.4]) completed sleep quality (Pittsburg Sleep Quality Index), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue), and sleepiness (Epworth Sleepiness Scale) measures. Emotional functioning was assessed ~5 years before (Behavior Problems Index <18 years old; Brief Symptom Inventory [BSI]>18 years old), and at time of the sleep survey (BSI only). Logistic/log binomial regression models examined relationships among diagnosis, treatment exposures, and emotional functioning on sleep variables, adjusting for age and BMI. In survivors, 35% reported <7-hr sleep duration, 29% reported sleep efficiency <85%, and 18% reported significant daytime sleepiness. Survivors were more likely to report poor sleep efficiency (prevalence ratio [PR] 1.26, 95% confidence interval 1.04–1.53), daytime sleepiness(PR 1.31, 1.01–1.71), and supplement use for sleep(PR 1.56, 1.09–3.60) than siblings. Leukemia survivors reported more delayed sleep onset latency(PR 1.36, 1.01–1.83) compared to bone cancer survivors. Exposure to ≥20 Gy cranial radiation was associated with sleep onset after 1am(PR 2.84, 1.75–4.59) compared to no cranial radiation. Abdominal radiation ≥30 Gy was associated with frequent nighttime awakenings(PR 1.37, 1.08–1.49). Relative to survivors without distress, survivors who developed emotional distress from baseline to follow-up evidenced poor sleep efficiency(PR 1.70, 1.40–2.08), restricted sleep time(PR 1.35, 1.12–1.62), fatigue(PR 2.11, 1.92–2.32), daytime sleepiness(PR 2.19, 1.71–2.82), snoring(PR 1.85, 1.08–3.16), and frequent sleep medication(PR 2.86, 2.00–4.09) and supplement use(PR 1.89, 1.33–2.69). Cancer survivors are more likely to experience poor sleep efficiency, daytime sleepiness, and supplement use than siblings. For survivors who report poor sleep, there is a greater likelihood of persistent or worsened emotional distress; distress management may improve sleep. NCI U24 CA55727 (PI:Armstrong).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.308
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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