Bibliographic record
Abstract
Head and neck cancers comprise 6 % of cancer cases worldwide. Oral cavity and oropharynx cancers are among the most common head and neck cancers. The treatments for these malignancies have improved over the past few decades and a significant portion of patients will experience long-term survival after diagnosis. Sleep problems are common and often severe in this group. Risk factors that predispose patients to head and neck cancer are also important co-factors in sleep disorders. Smoking, alcohol abuse, advanced age, hypothyroidism are among the risk factors that are common in both head and neck cancer patients and patients with sleep problems. The available data on the prevalence, severity and impact of sleep problems in this group is extremely limited currently. In the past two decades researchers have identified very high prevalence of sleep problems, before and after treatments. Sleep problems in form of obstructive sleep apnea and subjective sleep quality tend to correlate with xerostomia, depression, pain, presence of tracheostomy and feeding tubes among other factors. Diagnosis is based on subjective questionnaires and sleep studies. Treatments are available and effective. These include addressing pain and depression, correcting underlying hypothyroidism, alleviating xerostomia, treating obstructive sleep apnea and removing unnecessary tracheostomies and feeding tubes. Improving quality of sleep has shown to improve the overall quality of life and productivity of the survivorship group. Sleep problems are a fertile ground of research in head and neck oncology and more data is expected to be available on this topic in the coming years. In this chapter the incidence, diagnosis and treatment of sleep problems in oral cavity and oropharynx cancer patients is discussed. The available data is reviewed and analyzed. An algorithm is proposed to approach and treat sleep problems in these 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".