A systematic review of SF-6D health state valuation studies
Bibliographic record
Abstract
BACKGROUND: The short-form 6-dimension (SF-6D) is a preference-based measure designed to calculate quality-adjusted life-year (QALY). Preference-based measures are standardized multidimensional health state classifications with preference or utility weights elicited from a sample of the population. There is a concern that valuations may differ between countries because of differences in culture, thus invalidating the use of values obtained from one country to another. OBJECTIVE: To conduct a systematic review of elicitation methods and modeling strategies in SF-6D studies and to present a general comparison of dimensions' ordering among different countries. METHODS: We performed a systematic review of studies that developed value sets for the SF-6D. The data search was conducted in PubMed, ScienceDirect, Embase, and Scopus up to 8 September 2022. Quality of studies was assessed with the CREATE checklist. Methodological differences were identified, and the dimensions' ordering of the selected studies was analyzed by cultural and economic factors. RESULTS: From a total of 1369 entries, 31 articles were selected. This corresponded to 12 different countries and regions and 17 different surveys. Most studies used the standard gamble method to elicit health state preferences. Anglo-Saxon countries gave more importance to pain, while other countries have physical functioning as the highest dimension. As the economic level increases, people care less about physical functioning but more about pain and mental health. CONCLUSIONS: Value sets for the SF-6D are different from one country to another and there is a need to develop value sets for more countries to consider cultural and economic differences.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".