Health State Utilities and Disease Duration in Systemic Sclerosis: Is There an Association?
Bibliographic record
Abstract
OBJECTIVE: Health state utility values (HSUV) are used as weightings to calculate quality-adjusted life years in economic evaluations. Evidence suggests that patients' perceptions of a new diagnosis for a chronic disease, while initially poor, may improve over time. The objective of this study was to examine the association between disease duration and direct HSUV scores in patients with systemic sclerosis (SSc). METHODS: Our study included patients with SSc from a US SSc center. An interviewer administered direct HSUV techniques including the visual analog scale (VAS), time tradeoff (TTO), and standard gamble (SG). We calculated the Short Form 6D HSUV from the Medical Outcomes Study Short Form-36. Additional clinical and demographic variables were collected. RESULTS: The mean age of the SSc sample (n = 223) was 51 years (SD 16) with the majority being women (84%). Median disease duration was 5 years (interquartile range 1.5-9). Mean (SD) HSUV scores were 0.67 (0.19) for the VAS, 0.76 (0.28) for the TTO, 0.84 (0.22) for the SG, and 0.65 (0.13) for the SF-6D. In patients with early disease (defined as ≤ 2 yrs, n = 78), the mean HSUV values were 0.64 (VAS), 0.70 (TTO), 0.80 (SG), and 0.63 (SF-6D) versus for those with a longer disease duration: 0.69, 0.79, 0.87, and 0.67, respectively. In multivariate analysis, the SG measure showed a significant and positive association with disease duration measured as a continuous variable and using a threshold of 2 years (p = 0.047 and p = 0.023, respectively). CONCLUSION: Greater disease duration showed a positive association with a direct measure (SG) of utility elicitation after a period of 2 years.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".