Abstract
Fracture prediction models have been developed that can estimate an individual's cumulative or lifetime fracture risk on the basis of bone mineral measurements and other factors. Examples illustrate how estimates of cumulative fracture risk might be influenced by initial age and level of bone mass, as well as by anticipated bone loss rate. New data regarding bone loss rates, fracture costs, and fracture risk factors should be incorporated to improve the accuracy of existing models. Such models could then be used for cost-benefit analysis to explore optimal treatment strategies.
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