Changes to Osteoporosis Prevalence According to Method of Risk Assessment
Bibliographic record
Abstract
UNLABELLED: The impact of clinical risk factor-based absolute risk methods on the prevalence of high risk for osteoporotic fracture is unknown. We applied absolute risk methods to 6646 subjects and found that the prevalence of elderly women deemed to be at high risk increased substantially, whereas the overall prevalence was highly dependent on the threshold used to designate high risk. INTRODUCTION: Many groups have advocated using absolute risk methods that incorporate clinical risk factors to target patients for osteoporosis therapy. We examined how the application of such absolute risk classification systems influences the prevalence of those considered to be at high risk for osteoporotic fracture and compared these systems to one based solely on BMD. MATERIALS AND METHODS: Using 6646 subjects from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective, randomly selected, population-based cohort, we assessed three different systems for determining prevalence of high risk for osteoporotic fracture: a BMD-based system; a simplified risk factor system incorporating age, sex, BMD, and two clinical risk factors; and a comprehensive system, incorporating age, sex, BMD, and seven clinical risk factors. The 10-year absolute risks of incident fragility fracture were compared across systems using three different high-risk thresholds. RESULTS: The prevalence of a T score < or = -2.5 was 18.8% (95% CI: 17.7-19.9%) in women and 3.9% (95% CI: 3.0-4.7%) in men. Using a 15% 10-year risk of fracture threshold, the prevalence of women at high risk increased to 46.9% (95% CI: 45.4-48.4) and 42.5% (95% CI: 41.1-43.9) when the comprehensive and simplified risk factor classification systems were used, respectively. Using a 25% 10-year absolute risk threshold, the prevalence of high risk was similar to that of the BMD-based system, whereas the 20% threshold gave intermediate rates. All thresholds analyzed resulted in an increased prevalence of older women at high risk for fracture, whereas only the 15% 10-year risk of fracture threshold resulted in an increase in the prevalence of men at high risk. CONCLUSIONS: The application of risk factor-based systems results in an increased prevalence of older women at high risk. The prevalence of individuals at high risk may increase with changes to the methods used to determine those who are eligible for therapy. These data have important implications for the pattern of care and costs of treating osteoporotic fractures.
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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 | 0.024 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".