A systematic review of SF-6D health state valuation studies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.151 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it