| Title |
Comparison between various fracture risk assessment tools
|
|---|---|
| Published in |
Osteoporosis International, June 2013
|
| DOI | 10.1007/s00198-013-2409-3 |
| Pubmed ID | |
| Authors |
W. D. Leslie, L. M. Lix |
| Abstract |
The suboptimal performance of bone mineral density as the sole predictor of fracture risk and treatment decision making has led to the development of risk prediction algorithms that estimate fracture probability using multiple risk factors for fracture, such as demographic and physical characteristics, personal and family history, other health conditions, and medication use. We review theoretical aspects for developing and validating risk assessment tools, and illustrate how these principles apply to the best studied fracture probability tools: the World Health Organization FRAX®, the Garvan Fracture Risk Calculator, and the QResearch Database's QFractureScores. Model development should follow a systematic and rigorous methodology around variable selection, model fit evaluation, performance evaluation, and internal and external validation. Consideration must always be given to how risk prediction tools are integrated into clinical practice guidelines to support better clinical decision making and improved patient outcomes. Accurate fracture risk assessment can guide clinicians and individuals in understanding the risk of having an osteoporosis-related fracture and inform their decision making to mitigate these risks. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Finland | 1 | 1% |
| Spain | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 92 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 14% |
| Researcher | 12 | 13% |
| Student > Master | 11 | 12% |
| Student > Doctoral Student | 10 | 11% |
| Professor | 6 | 6% |
| Other | 20 | 21% |
| Unknown | 23 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 41 | 43% |
| Engineering | 6 | 6% |
| Computer Science | 5 | 5% |
| Nursing and Health Professions | 4 | 4% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 9 | 9% |
| Unknown | 28 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 June 2013.
All research outputs
#18,341,369
of 22,713,403 outputs
Outputs from Osteoporosis International
#2,714
of 3,600 outputs
Outputs of similar age
#147,265
of 196,319 outputs
Outputs of similar age from Osteoporosis International
#22
of 35 outputs
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