A Nested Case-Controlled Comparison of Telomere Length and Psychological Functioning in Breast Cancer Survivors with and without Insomnia Symptoms
Bibliographic record
Abstract
The ability to achieve sufficient restorative sleep is important in the maintenance of physical and mental health; however, disturbed sleep and insomnia symptoms are a common experience among women with breast cancer. In non-cancer populations, insufficient sleep quantity and quality has been associated with shortened telomere length (TL), a measure of accumulated cellular damage and human aging. This feasibility study compared TL in women previously diagnosed with breast cancer with clinically significant insomnia symptoms (n=70) to an age- and body mass index (BMI)-matched comparison group (n=70) of breast cancer survivors. Women with significant insomnia symptoms had higher levels of unemployment compared to women without insomnia. TL was positively skewed and shorter in the insomnia group (Median=6.000, S=1.000, standard error [SE]=0.287) than the control group (Median=6.195, S=-0.269, SE=0.287); however, this was not significant (p=0.29). Women with insomnia also reported significantly higher levels of depression (p<0.001), anxiety (p<0.001), and fatigue (p<0.001). This study provides the first measure of effect size and variability of TL in women with breast cancer and highlights the need for larger sample sizes to investigate the impact of insomnia and co-morbid symptom distress on cellular aging.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".