Comparison of the EQ-5D and SF-12 Health Surveys in a General Population Survey in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: The purposes of this analysis were to evaluate the construct validity of the EQ-5D and compare responses on the EQ-5D with the Physical Component Summary (PCS-12) and Mental Component Summary (MCS-12) scores of the SF-12 Health Survey. METHODS: Data were collected via a survey instrument mailed to 4,200 randomly selected subjects in the province of Alberta, Canada. The instrument contained the EQ-5D and SF-12 health surveys, with additional questions eliciting clinical and demographic information from the respondents. RESULTS: 1,555 respondents returned mailed questionnaires; 606 questionnaires were returned undeliverable. The SF-12 summary scores were calculated for 1,380 respondents. Analysis of the EQ-5D responses by demographic variables found significant differences among categories of age, gender, and self-reported chronic medical conditions. Corresponding dimensions and summary scores were more strongly related (eg, mobility and PCS-12; F ratio = 598.3, P < 0.001) than dissimilar dimensions (eg, mobility and MCS-12; F ratio = 18.8, P < 0.001). The EQ-5D index scores were moderately correlated with SF-12 summary scores (r = 0.41 for MCS-12 and r = 0.68 for PCS-12). For subjects reporting no problems on the EQ-5D, PCS-12 and MCS-12 scores were significantly lower for people reporting medical problems or feelings of depression. CONCLUSIONS: The results of this investigation generally supported the validity of the EQ-5D. However, an important ceiling effect was observed for the EQ-5D in this sample. The combination of the EQ-5D and SF-12 provides relatively broad coverage of important health domains and scores for various purposes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".