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.
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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 | 0.069 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| 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".