Management of Older Adults with Locally Advanced Head and Neck Cancer
Bibliographic record
Abstract
Thirty percent of patients with head and neck squamous cell carcinoma (HNSCC) are at least 70 years of age. This number continues to rise as life expectancy continues to increase. Still, older adults with HNSCC remain underrepresented in clinical trials, resulting in ambiguity on optimal management. Older adults are a complex patient population, often requiring increased support due to issues relating to functional and performance status, medical comorbidities, and medication management. Furthermore, in older adults with HNSCC, many of these conditions are independently associated with increased toxicity and worse outcomes. Toxicity in the older adult remains difficult to predict and to understand, and as treatment decisions are based on treatment tolerability, it is essential to understand the toxicities and how to minimize them. Novel predictive scores are being developed specifically for older adults with HNSCC to understand toxicity and to assist in personalized treatment decisions. There are clinical trials presently underway that are investigating shortened radiation courses and novel, less toxic systemic treatments in this population. In the forthcoming sections, we provide a detailed overview of the clinical data, treatment paradigms, and considerations in this population. This review provides a comprehensive overview of existing clinical data and clinical considerations in the older adult head and neck cancer population. Additionally, we provide a detailed overview of pertinent current and ongoing clinical trials, as well as future areas for investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".