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Attention Score in Context
| Title |
Measuring improvement in fracture risk prediction for a new risk factor: a simulation
|
|---|---|
| Published in |
BMC Research Notes, January 2018
|
| DOI | 10.1186/s13104-018-3178-z |
| Pubmed ID | |
| Authors |
Lisa M. Lix, William D. Leslie, Sumit R. Majumdar |
| Abstract |
Improvements in clinical risk prediction models for osteoporosis-related fracture can be evaluated using area under the receiver operating characteristic (AUROC) curve and calibration, as well as reclassification statistics such as the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) statistics. Our objective was to compare the performance of these measures for assessing improvements to an existing fracture risk prediction model. We simulated the effect of a new, randomly-generated risk factor on prediction of major osteoporotic fracture (MOF) for the internationally-validated FRAX® model in a cohort from the Manitoba Bone Mineral Density (BMD) Registry. The study cohort was comprised of 31,999 women 50+ years of age; 9.9% sustained at least one MOF in a mean follow-up of 8.4 years. The original prediction model had good discriminative performance, with AUROC = 0.706 and calibration (ratio of observed to predicted risk) of 0.990. The addition of the simulated risk factor resulted in improvements in NRI and IDI for most investigated conditions, while AUROC decreased and changes in calibration were negative. Reclassification measures may give different information than discrimination and calibration about the performance of new clinical risk factors. |
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 % |
|---|---|---|
| United States | 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 40% |
| Researcher | 2 | 20% |
| Student > Doctoral Student | 2 | 20% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 30% |
| Nursing and Health Professions | 2 | 20% |
| Agricultural and Biological Sciences | 1 | 10% |
| Biochemistry, Genetics and Molecular Biology | 1 | 10% |
| Unknown | 3 | 30% |
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 31 January 2018.
All research outputs
#17,927,741
of 23,018,998 outputs
Outputs from BMC Research Notes
#2,849
of 4,283 outputs
Outputs of similar age
#310,204
of 441,076 outputs
Outputs of similar age from BMC Research Notes
#81
of 135 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,283 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.