Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related mortality worldwide, with the prevalence of the disease continually rising. Therefore, identifying disease-modifying risk factors is critical, with increasing recognition of the impact of sleep quality/sleep disorders. This narrative review summarizes the evidence on the role of five domains of sleep on lung cancer incidence and progression: (i) sleep quality/duration, (ii) sleep disordered breathing, (iii) circadian rhythm disturbances, (iv) sleep-related movement disorders, and (v) personal, environmental, and social factors that modulate each of these associations. Epidemiological evidence supports reduced sleep duration, increased sleep duration, poor sleep quality, insomnia, obstructive sleep apnea, evening chronotype, peripheral limb movements in sleep, and less robustly for night shift work and restless leg syndrome to be associated with increased risk of lung cancer development, with potential impacts on cancer survival outcomes. Proposed mechanisms underlying the biological plausibility of these epidemiological associations are also explored, with common theories relating to immune dysregulation, metabolic alterations, reductions in melatonin, sympathetic overactivation, increased reactive oxygen species, production of protumorigenic exosomes, and inflammation. We also summarized potential treatments addressing impaired sleep quality/sleep disorders and their ability to attenuate the risk of lung cancer and improve cancer survival. Although evidence on reversibility is inconsistent, there are trends toward positive outcomes. Future research should focus on clinical trials to confirm cause and effect relationships, large epidemiologic studies for incidence/prognosis, clarification on the relative efficacy of treatment modalities, and more in vivo animal models to establish the molecular mechanisms underlying these relationships.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".