Proposal for Managing Cancer‐Related Insomnia: A Systematic Literature Review of Associated Factors and a Narrative Review of Treatment
Bibliographic record
Abstract
OBJECTIVE: Insomnia is common in patients with cancer. It has a multifactorial etiology that may include the disease process, adverse effects of anticancer therapies, and/or an association with other comorbidities. The purpose of this review was to identify risk factors for insomnia and suggest optimal management strategies. METHODS: We conducted a systematic literature review to elucidate the risk factors for insomnia and sleep disturbances in patients with solid tumors. The effects of sleep medications in this population were also described. RESULTS: A total of 75 publications were evaluated, including those on breast, lung, gynecologic, brain, head and neck, gastrointestinal, prostate, thyroid, and mixed cancers. We classified the factors related to insomnia or sleep disturbance in cancer into four categories: (1) patient demographic characteristics (e.g., age, marital or socioeconomic status); (2) mental state (e.g., depression or anxiety); (3) physical state (e.g., fatigue, pain, or restless legs syndrome); and (4) anticancer treatment-related (e.g., use of chemotherapy, opioids, or hormone therapy). Overall, literature on the pharmacologic treatment of insomnia is extremely limited, although some efficacy data for zolpidem and melatonin have been reported. CONCLUSIONS: Demographic characteristics, physical and mental distress, and anticancer treatments are all risks for insomnia in patients with cancer. The limited evidence base for pharmacologic therapy in this patient population means that healthcare professionals need to implement a comprehensive and multidisciplinary pathway from screening to management.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".