Implementing a Health Utility Assessment Platform to Acquire Health Utilities in a Hemodialysis Outpatient Setting: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Patients with end-stage kidney disease (ESKD) wait roughly 4 years for a kidney transplant. A potential way to reduce wait times is using hepatitis C virus (HCV)-viremic kidneys. OBJECTIVE: As preparation for developing a shared decision-making tool to assist patients with ESKD with the decision to accept an HCV-viremic kidney transplant, our initial goal was to assess the feasibility of using The Gambler II, a health utility assessment tool, in an ambulatory dialysis clinic setting. Our secondary goals were to collect health utilities for patients with ESKD and to explore whether the use of race-matched versus race-mismatched exemplars impacted the knowledge gained during the assessment process. METHODS: We used The Gambler II to elicit utilities for the following ESKD-related health states: hemodialysis, kidney transplant with HCV-unexposed kidney, and transplantation with HCV-viremic kidney. We created race exemplar video clips describing these health states and randomly assigned patients into the race-matched or race-mismatched video arms. We obtained utilities for these 3 health states from each patient, and we evaluated knowledge about ESKD and HCV-associated health conditions with pre- and postintervention knowledge assessments. RESULTS: A total of 63 patients with hemodialysis from 4 outpatient Dialysis Center Inc sites completed the study. Mean adjusted standard gamble utilities for hemodialysis, transplant with HCV-unexposed kidney, and transplantation with HCV-viremic kidney were 82.5, 89, and 75.5, respectively. General group knowledge assessment scores improved by 10 points (P<.05) following utility assessment process. The use of race-matched exemplars had little effect on the results of the knowledge assessment of patients. CONCLUSIONS: Using The Gambler II to collect utilities for patients with ESKD in an ambulatory dialysis clinic setting proved feasible. In addition, educational information about health states provided as part of the utility assessment process tool improved patients' knowledge and understanding about ESKD-related health states and implications of organ transplantation with HCV-viremic kidneys. A wide variation in patient health state utilities reinforces the importance of incorporating patients' preferences into decisions regarding use of HCV-viremic kidneys for transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".