A <b>c</b>ore outcome set for patient‐reported dysphagia for use in head and neck cancer clinical trials: An international multistakeholder Delphi study
Bibliographic record
Abstract
BACKGROUND: Measuring dysphagia-related patient-reported outcomes (PROs) in Head and Neck Cancer (HNC) patients is challenging due to dysphagia's multidimensional impact, causing inconsistency in outcome reporting. To address this issue, this study derived a consensus-based core outcome set (COS) for patient-reported dysphagia in HNC clinical trials where swallowing is a primary or secondary endpoint. METHODS: A sample of HNC clinicians, researchers, patients, and caregivers participated in a 2-Round Delphi technique. A Delphi survey, containing a comprehensive list of dysphagia-related PROs, was developed. In Round 1, participants rated item importance on a 5-point scale. Items rated ≥4 by >70% advanced to Round 2, where a consensus meeting addressed items with varied opinions, and the Delphi survey with remaining items was completed. Items rated ≥4 by >70% formed the final COS. RESULTS: Forty-five participants from nine countries were recruited. After Round 1, 40 items were excluded and 64 advanced to Round 2. After Round 2, a 7-outcome COS was established, comprising the domains of dysphagia symptoms, health status and quality of life. CONCLUSION: This study achieved consensus among HNC stakeholders on essential dysphagia PROs for HNC clinical trials. It is advisable to include these 7-core concepts in clinical trials involving people with HNC to facilitate treatment comparisons and data synthesis.
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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 | 0.484 | 0.403 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".