Sleep disturbances in palliative cancer patients attending a pain and symptom control clinic
Bibliographic record
Abstract
OBJECTIVE: The nature of sleep disturbances in palliative cancer patients has not been delineated clearly or fully understood due to limited clinical information. The purpose of this study was to describe sleep disturbance patterns, treatments, and communication in an advanced cancer outpatient population attending a pain and symptom control clinic. METHOD: One hundred oncology outpatients who came for consultation at a multidisciplinary pain and symptom control clinic were asked and agreed to complete a self-report questionnaire that elicited information about their sleeping habits, sleep concerns, sleep enhancement strategies, and related communication with health care providers. RESULTS: The majority of participants (72%) reported a wide variety of sleep disturbances, after cancer diagnosis, with the three most frequent elevated symptoms (> or = 5) being not feeling rested in the morning (72%), difficulty staying asleep (63%), and difficulty falling asleep (40%). Approximately one-fifth of participants (19%) reported having insomnia problems prior to their cancer diagnosis. In a correlational comparison with four other symptoms (i.e., fatigue, pain, anxiety, depression), the three highest correlations were between difficulty falling asleep and fatigue (r = 0.612), early awakening and fatigue (r = 0.596), and difficulty falling asleep and anxiety (r = 0.572). Fifty-three percent of participants reported using a variety of interventions for their sleep problems, the most frequent being sleep medication (37%). Of the 52 participants who reported an elevated level of concern about their sleeping difficulties (> or = 5), 48 (92%) discussed their concerns with a health care provider. However, of the 20 participants with elevated symptoms (> or = 5) and low levels of concern (<5), only 7 (35%) communicated their concerns to a health care provider. SIGNIFICANCE OF RESULTS: The results of this study underline the importance of routine clinical assessments to detect sleep problems and interventions designed specifically to improve the overall sleep quality of cancer patients.
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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 | 0.000 |
| 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) | 0.001 | 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".