Sleep disturbances in patients with lung cancer in Turkey
Bibliographic record
Abstract
INTRODUCTION: Sleep quality is known to be associated with the distressing symptoms of cancer. The purpose of this study was to analyze the impact of cancer symptoms on insomnia and the prevalence of sleep-related problems reported by the patients with lung cancer in Turkey. MATERIALS AND METHODS: Assesment of Palliative Care in Lung Cancer in Turkey (ASPECT) study, a prospective multicenter study conducted in Turkey with the participation of 26 centers and included all patients with lung cancer, was re-evaluated in terms of sleep problems, insomnia and possible association with the cancer symptoms. Demographic characteristics of patients and information about disease were recorded for each patient by physicians via face-to-face interviews, and using hospital records. Patients who have difficulty initiating or maintaining sleep (DIMS) is associated with daytime sleepiness/fatigue were diagnosed as having insomnia. Daytime sleepiness, fatigue and lung cancer symptoms were recorded and graded using the Edmonton Symptom Assessment Scale. RESULT: Among 1245 cases, 48.4% reported DIMS, 60.8% reported daytime sleepiness and 82.1% reported fatigue. The prevalence of insomnia was 44.7%. Female gender, patients with stage 3-4 disease, patients with metastases, with comorbidities, and with weight loss > 5 kg had higher rates of insomnia. Also, patients with insomnia had significantly higher rates of pain, nausea, dyspnea, and anxiety. Multivariate logistic regression analysis showed that patients with moderate to severe pain and dyspnea and severe anxiety had 2-3 times higher rates of insomnia. CONCLUSIONS: In conclusion, our results showed a clear association between sleep disturbances and cancer symptoms. Because of that, adequate symptom control is essential to maintain sleep quality in patients with lung cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".