1045 Impact Of Pre-treatment Sleep And Menopausal Status On Sleep Quality In The 12 Months Following A Breast Cancer Diagnosis
Bibliographic record
Abstract
Abstract Introduction Sleep disturbances are a prevalent and enduring problem in women who have completed treatment for breast cancer. Less is known about whether sleep during and after cancer treatment is influenced by pre-treatment sleep quality and menopausal status. The present study aims to examine the trajectory of sleep quality in the 12 months following a cancer diagnosis and assess whether trajectory is influenced by pre-treatment sleep quality and menopausal status. Methods Newly-diagnosed women (N=88) with non-metastatic BCa were recruited before beginning treatment. They completed the Pittsburgh Sleep Quality Index (PSQI) before treatment and 4, 8, and 12 months later. Women with a score ≥5 on the Pittsburgh Sleep Quality Index at treatment onset were classified as poor sleepers. Menopausal status (pre- or post-menopausal) was chart abstracted. A mixed ANOVA assessed the impact of pre-treatment sleep quality and menopausal status on sleep quality trajectory. Results The mean age of the sample was 60 years, 70% were classified as poor sleepers, and 72% were post-menopausal. There was a significant linear time by sleep quality interaction, F(1, 83)= 5.79, p =.02. Good sleepers experienced a greater initial worsening of sleep quality than poor sleepers. At 12 months, poor sleepers had returned to baseline levels whereas scores in good sleepers remained higher than baseline. The 3-way time x sleep quality x menopausal status and the 2-way time by menopausal status interactions were not significant. Conclusion Baseline sleep quality is a more powerful determinant of sleep trajectory during treatment than menopausal status. Early intervention is necessary to treat existing sleep problems and prevent the development of sleep problems in women with a history of good sleep. Support Dr. Garland is supported by a New Investigator Award and seed funding from the Beatrice Hunter Cancer Research Institute (BHCRI).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".