Survey of current functional outcomes assessment practices in patients with head and neck cancer: initial project of the head and neck research network.
Bibliographic record
Abstract
BACKGROUND: Functional outcomes assessment has become increasingly important in informing treatment decisions in the area of head and neck cancer. However, consistency of assessment methods across studies has been lacking. For the literature to inform clinical decision making, consensus regarding outcomes measurements is necessary. OBJECTIVE: The Head and Neck Research Network (HNRN) was founded in January 2008 to become a conduit for high-quality research in the area of functional outcomes in patients with head and neck defects. The present study surveyed experts in functional outcomes assessment to determine what are considered the most important tools for assessing speech and swallowing and what background patient characteristics are important to capture. DESIGN, PARTICIPANTS, AND MEASURES: Respondents to the online survey included 54 participants with a background in speech-language pathology, with the majority of respondents from the United States, Canada, and the United Kingdom. RESULTS AND CONCLUSIONS: The results from the survey indicated that clinicians consider both subjective and objective measures as important to use when assessing function. More advanced technical tools were often rated as less important; however, it also was noted that clinicians were most often not able to access these tools or were unfamiliar with them.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".