0828 Pre-Treatment Insomnia Symptoms and Perceived Cognitive Impairment in Newly Diagnosed Women with Early Stage Breast Cancer
Bibliographic record
Abstract
Insomnia and perceived cognitive impairment (PCI) are prevalent in breast cancer (BCa) survivors. These symptoms are often attributed to cancer treatment; however, recent evidence suggests that insomnia and PCI are present before treatment initiation. This study explores the associations between insomnia, PCI, sleep, mood, and fatigue in pre-treatment women with BCa. This study is part of a larger ongoing prospective study of sleep and cognition in women with non-metastatic breast cancer. Participants completed the Insomnia Severity Index (ISI), the Functional Assessment of Cancer Treatment-Cognition (FACT-Cog), the Hospital Anxiety and Depression Scale (HADS), the Pittsburgh Sleep Quality Index (PSQI), and the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF). Using participant’s pre-treatment data, a hierarchical regression model was used to examine associations between symptoms of insomnia, mood, fatigue, and PCI, after statistically adjusting for age and education. Zero-order and partial correlations were used to examine the importance of individual predictors. Data was collected prior to initiating cancer treatment from 86 women newly diagnosed with BCa. On average, women were 59 years old (range 30-80) and had 14 years of education (range 7-25). After adjusting for age and education, the model was significant [F(5, 76)=8.94, p<.001], accounting for 36% of the unique variance in PCI. Zero-order correlations indicated significant bivariate associations between PCI and insomnia severity (r=-.34), sleep quality (r=-.39), fatigue (r=.56) and depressed (r=.38), anxious mood (r=.37). After partitioning out variability from other independent variables, only fatigue remained significantly associated with PCI, accounting for 15.3% unique variance. A follow up hierarchical regression revealed that mental and general fatigue were the dimensions of fatigue significantly associated with PCI. Even before undergoing cancer treatment, sleep, mood, and fatigue are associated with PCI in pre-treatment women with BCa. Fatigue, particularly general fatigue (e.g. I am worn out) and mental fatigue (e.g. I make more mistakes than usual), have the strongest relationships with PCI. 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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".