Sleep–wake difficulties in community-dwelling cancer patients receiving palliative care: subjective and objective assessment
Bibliographic record
Abstract
OBJECTIVE: Prevalence rates of sleep difficulties in advanced cancer patients have varied widely across studies (12 to 96%), and none of these employed a diagnostic interview to distinguish different types of sleep-wake disorders. Moreover, very limited information is available on subjective and objective sleep parameters in this population. Our study was conducted in palliative cancer patients and aimed to assess rates of sleep-wake disorders and subsyndromal symptoms and to document subjective and objective sleep-wake parameters across various types of sleep-wake difficulties. METHOD: The sample was composed of 51 community-dwelling cancer patients receiving palliative care and having an Eastern Cooperative Oncology Group score of 2 or 3. Relevant sections of the Duke Interview for Sleep Disorders were administered over the phone. An actigraphic recording and a daily sleep diary were completed for 7 consecutive days. RESULTS: Overall, 68.6% of the sample had at least one type of sleep-wake difficulty (disorder or symptoms): 31.4% had insomnia and 29.4% had hypersomnolence as their main sleep-wake problem. Participants with insomnia as their main sleep difficulty had greater disruptions of subjective sleep parameters, while objectively-assessed sleep was more disrupted in patients with hypersomnolence comorbid with another sleep-wake difficulty.Significance of the Results:The high rates of sleep-wake difficulties found in this study indicate a need to screen more systematically for sleep-wake disorders, including insomnia and hypersomnolence, in both palliative care research and clinical practice, and to develop effective nonpharmacological interventions specifically adapted to this population.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".