Prevalence, putative mechanisms, and current management of sleep problems during chemotherapy for cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sleep problems are highly prevalent in cancer patients undergoing chemotherapy. This article reviews existing evidence on etiology, associated symptoms, and management of sleep problems associated with chemotherapy treatment during cancer. It also discusses limitations and methodological issues of current research. The existing literature suggests that subjectively and objectively measured sleep problems are the highest during the chemotherapy phase of cancer treatments. A possibly involved mechanism reviewed here includes the rise in the circulating proinflammatory cytokines and the associated disruption in circadian rhythm in the development and maintenance of sleep dysregulation in cancer patients during chemotherapy. Various approaches to the management of sleep problems during chemotherapy are discussed with behavioral intervention showing promise. Exercise, including yoga, also appear to be effective and safe at least for subclinical levels of sleep problems in cancer patients. Numerous challenges are associated with conducting research on sleep in cancer patients during chemotherapy treatments and they are discussed in this review. Dedicated intervention trials, methodologically sound and sufficiently powered, are needed to test current and novel treatments of sleep problems in cancer patients receiving chemotherapy. Optimal management of sleep problems in patients with cancer receiving treatment may improve not only the well-being of patients, but also their prognosis given the emerging experimental and clinical evidence suggesting that sleep disruption might adversely impact treatment and recovery from cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it