Quality of life and health-related utility after head&neck cancer surgery
Bibliographic record
Abstract
Abstract Purpose This work describes the methodology adopted and the results obtained in a utility elicitation task. The purpose was to elicit utility coefficients (UCs) needed to calculate quality-adjusted life years for a cost/utility analysis of TORS (Trans-Oral robotic Surgery) versus TLM (Trans-oral Laser Microsurgery), which are two minimally-invasive trans-oral surgery techniques for head & neck cancers. Methods Since the economic evaluation would be conducted from the point of view of the Swiss healthcare system, Swiss people (healthy volunteers) have been interviewed in order to tailor the model to that specific country. The utility elicitation was performed using a computerized tool (UceWeb). Standard gamble and rating scale methods were used. Results UCs have been elicited from 47 individuals, each one providing values for 18 health states, for a total of 1692 expected values. Health states, described using graphical factsheets, ranged from remission to palliative care. Elicited UCs were different among states, ranging from 0.980 to 0.213. Those values were comparable to previously published results from a Canadian population, except for states related to recurrent disease (local, regional, and distant), and palliation, where the Swiss population showed lower utility values. Conclusion From a methodological point of view, our study shows that the UceWeb tool can be profitably used for utility elicitation from healthy volunteers. From an application point of view, the study provides utility values that can be used not only for a specific cost-utility analysis, but for future studies involving health states following trans-oral head & neck surgery. Moreover, the study confirms that some UCs vary among countries, demanding for tailored elicitation tasks.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".