The psychometric properties of patient preferences in osteoporosis.
Bibliographic record
Abstract
OBJECTIVE: Osteoporosis is a chronic disease manifested by wrist, vertebral, and hip fractures that results in significant morbidity and burden to society. About 30% of postmenopausal women have osteoporosis according to the WHO criteria. Women with one vertebral fracture have a 4-fold increased risk of a subsequent fracture. The goal of treatment is to prevent fractures and improve quality of life. Preferences or utilities are now recommended for incorporating quality of life into evaluations of the cost effectiveness of new therapeutic interventions. We evaluated the psychometric properties of preference based measures in osteoporosis. METHODS: Preference scenarios were constructed with a health state classification system. The reliability and validity of the feeling thermometer and the standard gamble was assessed by interviewing 42 women from 4 different patient groups. The sensitivity to change of the feeling thermometer and standard gamble was compared with the Health Utilities Index Mark 2 (HUI2) and SF-36. All subgroups were reassessed about 2 months after their first interview. RESULTS: Preference measurement was feasible in women of different age groups. The reliability coefficients for health states ranged from 0.65 to 0.87. The preference scores for the marker states demonstrated content validity. Convergent validity of the feeling thermometer was supported by a significant correlation with the HUI2 (r = 0.38, p < 0.05) and the physical health summary of the SF-36 (r = 0.56, p < 0.005). The standard gamble did not correlate with the HUI2 (r = 0.15) or the feeling thermometer (r = 0.09), but correlated with 2 domains of the SF-36. The preference measures were sensitive to change, with the efficiency scores ranging from 0.78 to 1.0. CONCLUSION: Preference measurements in the evaluation of osteoporosis are feasible. The feeling thermometer and standard gamble appear to be related to different aspects of health related quality of life. Both instruments were sensitive to change over a 2 month period.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".