Preference-based SF-6D Scores Derived From the SF-36 and SF-12 Have Different Discriminative Power in a Population Health Survey
Bibliographic record
Abstract
OBJECTIVE: To compare the discriminative power of the SF-6D index scores derived from the SF-36 (SF-6D36) and SF-12 (SF-6D12) in the general population. METHODS: Data from the National Health Measurement Study were used. The F statistic was used to compare the relative efficiency of the SF-6D36 and SF-6D12, as well as the EQ-5D, HUI2, and HUI3 index scores, in discriminating between respondents with and without 1 of the 11 chronic medical conditions. The efficiency of the multiattribute health classification systems of the study instruments was measured using the Shannon index (H'). The relative efficiency of the SF-6D36 and SF-6D12 was also compared in respondents who were on the ceilings of the EQ-5D, HUI2, and HUI3 scales. RESULTS: The SF-6D36 score was systematically lower than the SF-6D12 score at the group level (range, 0.022-0.036). The SF-6D36 exhibited higher discriminative power in 8 and 5 conditions than the SF-6D12 and all other index scores, respectively. The SF-6D36 had higher H' values than the SF-6D12 in the dimensions of physical functioning (1.73 vs. 0.78), mental health (1.70 vs. 1.39), and bodily pain (2.16 vs. 1.56) as well as than all other instruments in similar health dimensions. In respondents reporting full health on the EQ-5D, HUI2, or HUI3, the SF-6D36 better discriminated between those with and without medical conditions than the SF-6D12. CONCLUSIONS: The SF-6D derived from the SF-36 is more discriminative than that derived from the SF-12 and is therefore preferred for use in population health surveys where a preference-based health index is needed.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".