Oral and dental health in head and neck cancer survivors
Bibliographic record
Abstract
Therapeutic improvements and epidemiologic changes in head and neck cancer (HNC) over the last three decades have led to increased numbers of survivors, resulting in greater need for continuing management of oral and dental health in this population. Generally, the HNC patient oral health needs are complex, requiring multidisciplinary collaboration among oncologists and dental professionals with special knowledge and training in the field of oral oncology. In this review, we focus on the impact of cancer treatment on oral health, and the oral care protocols recommended prior to, during and after cancer therapy. The management of oral complications such as mucositis, pain, infection, salivary function, taste and dental needs are briefly reviewed. Other complications and their management, including osteonecrosis of the jaw and recurrent/new primary malignancies are also described. This review offers clinical protocols and information for medical providers to assist in understanding oral complications and their management in HNC patients and survivors, and their oral and dental health care needs. Oral and dental care is impacted by the patient's initial oral and dental status, as well as the specific cancer location, type, and its treatment; thus, close communication between the dental professional and the oncology team is required for appropriate therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".