1037 One Year Trajectory of Insomnia and Comorbid Symptoms in Women With Early Stage Breast Cancer
Bibliographic record
Abstract
Abstract Introduction Insomnia symptoms are a common problem and are often comorbid with hot flashes, fatigue, anxiety, and depression following a breast cancer diagnosis. The present study examined changes in insomnia severity and comorbid symptoms in the year following diagnosis. Methods This study is part of a larger prospective observational cohort study of 100 women with early stage breast cancer. Insomnia symptoms were measured using the Insomnia Severity Index, fatigue was measured using the Multidimensional Fatigue Symptom Inventory-Short Form, anxiety and depression were assessed using the Hospital Anxiety and Depression Scale, and hot flashes were assessed using the Hot Flash Related Daily Interference Scale. Assessments were performed shortly after diagnosis, 4, 8, and 12 months. A series of repeated measures within subjects ANOVAs were performed to assess changes in symptoms over time. Results Among 100 women with breast cancer, 45% reported at least mild insomnia symptoms. There were significant quadratic effects of time on insomnia severity, F(3, 297)= 12.776, p ≤ .001, depression (F[3, 297]= 4.409, p = .005), and fatigue (F[3, 297]= 7.995, p ≤ .001). These symptoms initially worsen and then improve throughout the year, but they do not rebound to pre-treatment levels. Interference from hot flashes worsens and endures for longer than other symptoms but does begin to show improvement one year post-diagnosis (F[3, 297]= 12.110, p ≤ .001). The were no time effects for anxiety (F[3, 297] = 1.4, p = .243). Conclusion In general, women treated for breast cancer can expect insomnia and comorbid symptoms to worsen then improve, but not recover, during the first year after a breast cancer diagnosis. Early efforts to educate women and manage symptoms could prevent insomnia and other issues from becoming persistent problems. Support Dr. Garland is supported by a Scotiabank 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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".