Top 10 research priorities in head and neck cancer: Results of an Alberta priority setting partnership of patients, caregivers, family members, and clinicians
Bibliographic record
Abstract
BACKGROUND: The epidemiology, etiology, and management of head and neck cancer are evolving. Understanding the perspectives and priorities of nonresearchers regarding treatment uncertainties is important to inform future research. METHODS: Using the James Lind Alliance approach, patients, caregivers, and clinicians responded to a survey regarding their unanswered questions about treating and managing head and neck cancer. Distinct uncertainties were extracted from responses and sorted into themes. Uncertainties already answered in the literature were removed. Those remaining were ranked by patients and clinicians to develop a short list of priorities, which were discussed at a workshop and reduced to the top 10. RESULTS: One hundred sixty-one respondents posed 818 uncertainties, culminating in 77 for interim ranking and 27 for discussion at a workshop. Participants reached consensus on the top 10, which included questions on prevention, screening, treatment, and quality of life. CONCLUSION: Nonresearchers can effectively collaborate to establish priorities for future research in head and neck cancer.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".