Quality of life in hypertension: the SF-12 compared to the SF-36.
Bibliographic record
Abstract
BACKGROUND: The SF-36 has frequently been used to measure health related quality of life (HRQOL) in hypertension. Recently, the SF-12, a shorter form of the SF-36, has been proposed. However, the validity of the SF-12 in hypertension has not yet been assessed. OBJECTIVES: To determine the extent to which the SF-12 provides similar measurements of HRQOL to those of the SF-36 in hypertensive individuals. METHODS: A study assessing the impact of a pharmacy-based intervention program on hypertensive individuals served as background for this study. One hundred and twelve individuals participated in this study. We compared the SF-36 with the SF-12 on item scores and summary measures using intraclass correlation coefficients (ICC), Pearson correlation coefficients and linear regression. RESULTS: The concordance between the SF-12 and the SF-36 on both physical (ICC=0.88) and mental (ICC=0.92) component summary scores (PCS and MCS respectively) is high and the relationship is linear and positive. Most of the variance in the SF-36 PCS (R2=0.78) and MCS (R2=0.85) can be explained by their SF-12 counterparts. The SF-12 PCS and MCS are the only significant predictor variables for the corresponding measure of the SF-36. CONCLUSIONS: The SF-12 appears to be a valid alternative to the SF-36 for clinical practice or research purposes when studying hypertensive individuals and their treatment.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".