Japanese value set for the Functional Assessment of Cancer Therapy Eight Dimension (FACT-8D) cancer-specific preference-based quality of life instrument
Bibliographic record
Abstract
PURPOSE: The Functional Assessment of Cancer Therapy General (FACT-G) questionnaire is frequently used to assess health-related quality of life (HRQOL) in cancer patients. However, data obtained using the FACT-G cannot be directly used to calculate quality-adjusted life years (QALYs). The newly developed FACT Eight Dimensions (FACT-8D) is a preference-based measure that generates health utilities scores from 9 of the 27 FACT-G items, representing eight HRQOL domains (Nausea, Pain, Fatigue, Sleep, Work, Worry, Sadness, Support from family/friends). This study aimed to create a Japanese FACT-8D value set. METHODS: A cross-sectional online survey of the Japanese general population recruited participants via a Japanese online panel, quota sampled by age (≥ 18 years) and sex. FACT-8D valuation data were collected with a discrete choice experiment. The valuation task required each participant to consider 16 pairs of hypothetical health states, randomly assigned per participant from 800 choice-sets. Preference weights were obtained from conditional logit models by dividing estimated HRQOL coefficients by the life duration coefficient. RESULTS: Data from 2320 participants were used to assess sample representativeness and estimate the Japanese value set. All preference weights other than Worry Level 2 were negative and increased in absolute terms in progressively higher levels of adverse HRQOL impact. The most influential domains for health utilities were Pain and Nausea, followed by Work problems. Fatigue, Sleep, Support, Sadness, and health Worry had moderate influences on health utilities. The lowest score, for the pit state [55555555], was − 0.60. This value is much lower than that of the EORTC QLU-C10D pit state [4444444444], -0.22. Health states were consistently scored higher in the USA, Australia and the UK than in Japan. Canadian health states are generally lower than for Japan, but not universally so. CONCLUSIONS: We established the Japanese FACT-8D value set based on the internationally common protocol. The value set provides another option for quantifying health utilities for cancer outcomes. This contributes to improving the feasibility of deriving health utilities from the widely used FACT-G.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".