Quality of life and health-related utility after trans-oral surgery for head and neck cancers
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to assess utility coefficients of health states following two minimally invasive surgical approaches for head and neck cancer, namely trans-oral robotic surgery and trans-oral laser microsurgery. Those utility coefficients will be later exploited in an economic evaluation study comparing the two approaches. METHODS: The above cited economic evaluation will be done from the Swiss healthcare system perspective and, as such, Swiss healthcare professionals were interviewed to elicit utility coefficients. Health states, ranging from remission to palliative care, were described using clinical vignettes. A computerized tool (UceWeb) implementing standard gamble and rating scale methods was used. RESULTS: Utility coefficients for 18 different health states were elicited with the two methods from 47 individuals, for a total of 1692 values. Elicited values varied from 0.980 to 0.213. Comparison with values elicited in previous studies show the need for population-specific elicitation, mainly for the worst health states. CONCLUSION: Herein we report health utility coefficients for the Swiss population for health states following minimally invasive trans-oral surgery. This study provides utility values that can be used not only for a specific cost-utility analysis, but also for future studies involving the same health states.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".