Abstract 1830: Factors associated with fatigue, sleep dysfunction, and joint symptoms in breast cancer survivors
Bibliographic record
Abstract
Abstract Background: Fatigue, sleep dysfunction, and joint symptoms may negatively impact quality of life in breast cancer survivors (BCS). Prior studies examining associated factors have yielded inconsistent results. Objective: Determine demographic, medical, exercise-related, body composition and serum inflammatory factors associated with fatigue, sleep dysfunction, and/or joint symptoms in BCS. Methods: Post-hoc analysis of baseline data completed by 37 BCS enrolled in a pre/post intervention study or randomized trial. Self administered survey assessed demographics, medical variables, self-efficacy (i.e., confidence in ability to exercise), fatigue (FSI), sleep dysfunction (PSQI), and joint symptoms (WOMAC). Sleep was also objectively assessed with an accelerometer. Body composition was assessed by body mass index (BMI), waist-to-hip ratio (WHR), and percent body fat (bioelectrical impedance). Cardio respiratory fitness was assessed with sub maximal treadmill test and muscle strength with back/leg extensor dynamometer. Multiplex high sensitivity assay or ELISA was performed on fasting serum samples to determine levels of cytokines or other markers related to body composition. Due to the skewed nature of several variables, Spearman correlations (r) were performed. A significant p value was set at <.05. Results: Participant mean age and education were 55 ± 10.1 and 15 ± 2.8 years, respectively. The majority (87%) were of European heritage with breast cancer stage distribution being I (51%), II (38%) or III (11%). Daily amount of fatigue was significantly associated with months since chemotherapy (r=.43, p = .027), number of comorbidities (r = .33, p = .0497), and fitness (r = -.35, p = .033). Depending on the subscale, self reported sleep dysfunction was associated with income (r = -.36, p = .029), number of comorbidities (r = .37, p = .028), WHR (r = .45, p = .005), tumor necrosis factor (TNF) α (r = .36, p = .029), interleukin (IL)-8 (r = -.45, p = .006), IL-10 (r = -.42, p = .011), TNF α /IL-10 ratio (r = .54, p = <.001), insulin (r = .48, p = .003), and adiponectin (r = -.36, p = .031). Time awake while in bed (accelerometer) was significantly associated with race (r = -.39, p = .019), muscle strength (r = -.35, p = .034), fitness (r = -.48, p = .003), adiponectin (r = -.39, p = .020), and monocytic chemotactic protein (MCP)-1 (r = -.33, p = .047). Time asleep was associated with race (r = .36, p = .031) and leptin (r = -.36, p = .031). Joint symptoms were significantly associated with age (r = .43, p = .008), education (r = -.34, p = .041), number of comorbidities (r = .60, p = <.001), self-efficacy (r = -.55, p = .003) and BMI (r = .33, p = .048). Conclusion: Fatigue and sleep dysfunction were significantly associated with medical factors and fitness. Sleep dysfunction was also associated with demographics, inflammation, strength, and body composition. Joint symptoms were associated with demographics, medical factors, self-efficacy and BMI. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1830. doi:10.1158/1538-7445.AM2011-1830
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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 | 0.001 |
| 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) | 0.004 | 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".