Health state utilities associated with major clinical events in the context of secondary hyperparathyroidism and chronic kidney disease requiring dialysis
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease (CKD) and secondary hyperparathyroidism (SHPT) who require dialysis are at increased risk for cardiovascular events and bone fractures. To assist in economic evaluations, this study aimed to estimate the disutility of these events beyond the impact of CKD and SHPT. METHODS: A basic one-year health state was developed describing CKD and SHPT requiring dialysis. Further health states added acute events (cardiovascular events, fractures, and surgical procedures) or chronic post-event effects. Acute health states described a year including an event, and chronic health states described a year subsequent to an event. General population participants in Canada completed time trade-off interviews from which utilities were derived. Pairwise comparisons were made between the basic state and event, and between comparable health states. RESULTS: A total of 199 participants (54.8% female; mean age = 46.3 years) completed interviews. Each health state had ≥130 valuations. The mean (SD) utility of the basic health state was 0.60 (0.34). For acute events, mean utility differences versus the basic state were: myocardial infarction, -0.06; unstable angina, -0.05; peripheral vascular disease (PVD) with amputation, -0.33; PVD without amputation, -0.11; heart failure, -0.14; stroke, -0.30; hip fracture, -0.14; arm fracture, -0.04; parathyroidectomy, +0.02; kidney transplant, +0.06. Disutilities for chronic health states were: stable angina, -0.09; stroke, -0.27; PVD with amputation, -0.30; PVD without amputation, -0.12; heart failure, -0.14. CONCLUSIONS: Cardiovascular events and fractures were associated with lower utility scores, suggesting a perceived decrease in quality of life beyond the impact of CKD and SHPT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".