Prevalence of sleep disorders in patients with advanced cancer: a cross-sectional study
Bibliographic record
Abstract
Background: Patients with advanced cancer are more susceptible to develop sleep disorders like insomnia, restlessness, hypersomnolence, and sleep apnea due to a series of stressful events and side effects of chemotherapeutic agents. Poor sleep quality is associated with bad cancer outcomes and substandard quality of life. The authors assessed the prevalence of sleep disorders among advanced cancer patients in a tertiary center in Nepal. Methods: Patients with stage three and four solid malignancies were enrolled from February 2023 to July 2023 to assess their sleep status. The data were collected using the Pittsburgh Sleep Quality Index (PSQI) questionnaire, analyzed using the Statistical Package for the Social Sciences (SPSS) version 27, and subgroup exploration was done to assess the relationship of poor sleep quality with gender, marital status, malignancy type, and treatment received. An ethical clearance was obtained from the Institutional Review Committee (IRC). Results: The authors evaluated data from 357 patients in the study. Of them, 58.3% were female and 41.7% were male. The mean age of the patients was 51.1 years. Among total cancer patients, 56% had significant sleep disorders. A significant association was observed between the quality of sleep and gender, type of malignancy, and treatment methods ( p value <0.05). A majority of the patients demonstrated increased sleep latency, struggling to fall asleep swiftly. Conclusions: More than half of the patients had poor sleep, which has an adverse impact on the prognosis of the disease and quality of life of cancer patients. Therefore, this aspect of cancer management requires special consideration for better quality of life and appropriate end-of-life care.
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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) | 0.000 | 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) | 0.000 | 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".