International Variations in Hip Fracture Probabilities: Implications for Risk Assessment
Bibliographic record
Abstract
It is recommended that intervention thresholds should be based on absolute fracture risk, but there is a large variation in hip fracture incidence from different regions of the world. The aim of this study was to examine heterogeneity of hip fracture probability in different regions from recent estimates of hip fracture incidence and mortality to adjust intervention thresholds. Ten-year probabilities of hip fracture were computed in men and women at 10-year intervals from the age of 50 years and lifetime risks at the age of 50 years from the hazard functions of hip fracture and death. Lifetime risk at the age of 50 years varied from 1% in women from Turkey to 28.5% in women from Sweden. High lifetime risks in women were associated with high lifetime risks in men (r = 0.83). There also were significant correlations of 10-year risk at any age between men and women. Ten-year probability was standardized to that of men and women from Sweden (set at 1.0). There was a 15-fold range in 10-year probability from 1.24 in Norway to 0.08 in Chile. Countries were categorized by 10-year probabilities comprising very high risk (Norway, Iceland, Sweden, Denmark, and the United States), high risk (China [Taiwan [TW]], Germany, Switzerland, Finland, Greece, Canada, The Netherlands, Hungary, Singapore, Italy, United Kingdom, Kuwait, Australia, and Portugal), medium risk (China [Hong Kong [HK]], France, Japan, Spain, Argentina, and China), and low risk (Turkey, Korea, Venezuela, and Chile). The categorization of hip fracture probabilities can be used to adjust intervention thresholds based on age, sex, and relative risk from a reference population such as Sweden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".