Sleep Problems and Quality of Life in Breast Cancer Patients
Bibliographic record
Abstract
Background: Sleep problems are frequently observed in breast cancer patients. However, the relationship between sleep quality and overall quality of life (QoL) and the specificity of different sleep-related questionnaires have not yet been adequately studied in breast cancer patients. Methods: The sample of this cross-sectional study consisted of 533 breast cancer patients, recruited in a German rehabilitation clinic, with a mean age of 52.3 years (SD = 12.5 years). The following three sleep-related questionnaires were used: the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index (ISI), and the Jenkins Sleep Scale (JSS). In addition, we used the QoL instrument EORTC QLQ-C30. Results: Sleep quality was poor in this sample of breast cancer patients. The effect sizes d, indicating the difference in sleep quality between the patient sample and the general population, were between 0.97 and 1.76 (p < 0.001). QoL was impaired in all components (p < 0.001); the impairment in the dimension of sleep quality (d = 1.70) was among the highest. Sleep quality was correlated with all components of QoL. The comparison of the three sleep-related questionnaires showed that the results obtained in oncological studies partly depend on the instrument used. Conclusion: As the burden of sleep problems is high, screening for sleep problems in breast cancer patients is important.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".