Sleep Quality in Head and Neck Cancer
Bibliographic record
Abstract
BACKGROUND: Patients with head and neck cancer often experience impaired sleep. Moreover, the treatment may negatively affect sleep quality. The aim of this observational study was to evaluate the sleep quality after treatment for head and neck cancer, and its relationship with quality of life and psychological distress. METHODS: A total of 151 patients who underwent treatment for head and neck cancer at our department were included in the study. Quality of life, sleep quality, risk of sleep apnea, sleepiness, pain, and psychological distress were assessed by means of specific questionnaires. RESULTS: The median follow-up was 30 months. Poor sleep quality was observed in 55.6% of the cases. An association between PSQI global sleep quality and EORTC global health status was found. The DT, HADS anxiety, and HADS depression scores were associated to PSQI global score, sleep quality, sleep latency, sleep disturbances, and daytime dysfunction. CONCLUSIONS: Sleep disturbances, particularly OSA and insomnia, are frequent in HNC patients, and significantly impact their quality of life and psychological well-being. Given the effect of sleep on overall well-being, addressing sleep disorders should be a priority in the care of HNC patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".