Insomnia in the Context of Cancer: A Review of a Neglected Problem
Bibliographic record
Abstract
This article reviews the evidence on the diagnosis, epidemiology, etiology, and treatment of insomnia in the context of cancer and proposes several areas for future research. Clinical and diagnostic features of insomnia are described and prevalence estimates of insomnia complaints in cancer patients are summarized. Then, potential etiologic factors (ie, predisposing, precipitating, and perpetuating factors) and consequences of insomnia (ie, psychologic, behavioral, and health impact) in the context of cancer are discussed. Finally, pharmacologic and psychologic treatments previously shown effective to treat insomnia in healthy individuals are discussed as valuable treatment options for cancer patients as well. Because long-term use of hypnotic medications is associated with some risks (eg, dependence), it is argued that psychologic interventions (eg, stimulus control, sleep restriction, cognitive therapy) are the treatment of choice for sleep disturbances in the context of cancer, especially when it has reached a chronic course. However, the efficacy of these treatments has yet to be verified specifically in cancer patients.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".