Estimating health state utilities associated with a rare disease: Familial chylomicronemia syndrome (FCS)
Bibliographic record
Abstract
Aims: Familial Chylomicronemia Syndrome (FCS) is a rare genetic disorder with no currently approved therapies. Treatments are in development, and cost-utility analyses will be needed to examine their value. These models will require health state utilities representing FCS. Therefore, the purpose of this study was to estimate utilities for FCS and an associated episode of acute pancreatitis (AP). Methods: Because it is not feasible to gather a large enough sample of patients with this extremely rare condition to complete standardized preference-based measures, vignette-based methods were used to estimate utilities. In time trade-off interviews, general population participants in the UK and Canada valued health state vignettes drafted based on literature review, clinician input, and interviews with patients. Four health states described variations of FCS. A fifth health state, describing AP, was added to one of the other health states to evaluate its impact on utility. Results: A total of 308 participants provided utility data (208 UK; 100 Canada). Mean utilities for FCS health states ranged from 0.46 to 0.83, with higher triglycerides, more severe symptoms, and a history of AP associated with lower utility values. The disutility (i.e., utility decrease) of AP ranged from -0.17 to -0.25, with variations depending on the health state to which it was added. Utility means were similar in the UK and Canada. Conclusions: The vignette-based approach is useful for estimating utilities of a rare disease. The health state utilities derived in this study would be useful in models examining cost-effectiveness of treatments for FCS.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".