0823 Understanding Insomnia In National Cohort Of Young Adult Cancer Survivors: Results From The Yacprime Study
Bibliographic record
Abstract
Insomnia prevalence varies throughout the cancer trajectory. There is limited understanding of insomnia in the young adult (YA) cancer population, how risk for insomnia changes after diagnosis, and what factors contribute to this risk in YA cancer survivors. YAs between the age of 18-39 (N=508) from across Canada completed the Young Adults with Cancer in their PRIME (YACPRIME) study. Likelihood of insomnia disorder (yes/no) was calculated based on responses to the Pittsburgh Sleep Quality Index's questions regarding subjective poor sleep and difficulty falling or remaining asleep 3 or more times per week. Demographic and disease characteristics were self-reported. Physical and mental health was derived from the SF-12. Univariate logistic regressions assessed the relationship between insomnia likelihood, demographic, clinical, and disease-specific variables. Significant covariates were carried forward into a multivariate model. Survivors were an average of 33 years of age, primarily female (87%), and 3 years post-diagnosis (range=0-28 years) for a range of common cancers. Immediately post-cancer diagnosis, 52% of YAs are likely to have insomnia disorder and 40% of those with insomnia report using sleep medications. For every additional year post-diagnosis, the likelihood of YAs continuing to experience insomnia disorder decreases by 9%. Compared to those with good physical health, those with poor physical health were more likely to be in the insomnia category (OR=1.81, 95% CI 1.06-3.10). Compared to those with good mental health, those with poor mental health also were more likely to be in the insomnia group (OR=3.96, 95% CI 1.74-9.00). When entered into a multivariate model, only poor mental health remained a significant predictor of insomnia in YAs (Adjusted OR=3.73, 95% CI 1.59-8.75). Sleep improves over time for most YA cancer survivors but poor mental health is associated with insomnia regardless of where they are in the cancer trajectory. Dr. Garland has a New Investigator Award from the Beatrice Hunter Cancer Research Institute (BHCRI). Funding also provided by the Newfoundland and Labrador Support Unit for People and Patient-Oriented Research and Trials (NLSUPPORT).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".