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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Risk prediction model for knee pain in the Nottingham community: a Bayesian modelling approach
|
|---|---|
| Published in |
Arthritis Research & Therapy, March 2017
|
| DOI | 10.1186/s13075-017-1272-6 |
| Pubmed ID | |
| Authors |
G. S. Fernandes, A. Bhattacharya, D. F. McWilliams, S. L. Ingham, M. Doherty, W. Zhang |
| Abstract |
Twenty-five percent of the British population over the age of 50 years experiences knee pain. Knee pain can limit physical ability and cause distress and bears significant socioeconomic costs. The objectives of this study were to develop and validate the first risk prediction model for incident knee pain in the Nottingham community and validate this internally within the Nottingham cohort and externally within the Osteoarthritis Initiative (OAI) cohort. A total of 1822 participants from the Nottingham community who were at risk for knee pain were followed for 12 years. Of this cohort, two-thirds (n = 1203) were used to develop the risk prediction model, and one-third (n = 619) were used to validate the model. Incident knee pain was defined as pain on most days for at least 1 month in the past 12 months. Predictors were age, sex, body mass index, pain elsewhere, prior knee injury and knee alignment. A Bayesian logistic regression model was used to determine the probability of an OR >1. The Hosmer-Lemeshow χ(2) statistic (HLS) was used for calibration, and ROC curve analysis was used for discrimination. The OAI cohort from the United States was also used to examine the performance of the model. A risk prediction model for knee pain incidence was developed using a Bayesian approach. The model had good calibration, with an HLS of 7.17 (p = 0.52) and moderate discriminative ability (ROC 0.70) in the community. Individual scenarios are given using the model. However, the model had poor calibration (HLS 5866.28, p < 0.01) and poor discriminative ability (ROC 0.54) in the OAI cohort. To our knowledge, this is the first risk prediction model for knee pain, regardless of underlying structural changes of knee osteoarthritis, in the community using a Bayesian modelling approach. The model appears to work well in a community-based population but not in individuals with a higher risk for knee osteoarthritis, and it may provide a convenient tool for use in primary care to predict the risk of knee pain in the general population. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 17% |
| Student > Master | 9 | 14% |
| Student > Ph. D. Student | 4 | 6% |
| Student > Postgraduate | 3 | 5% |
| Lecturer | 3 | 5% |
| Other | 9 | 14% |
| Unknown | 26 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 20% |
| Nursing and Health Professions | 4 | 6% |
| Business, Management and Accounting | 3 | 5% |
| Engineering | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 13 | 20% |
| Unknown | 28 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 07 November 2017.
All research outputs
#16,051,091
of 25,382,440 outputs
Outputs from Arthritis Research & Therapy
#2,337
of 3,380 outputs
Outputs of similar age
#185,466
of 323,360 outputs
Outputs of similar age from Arthritis Research & Therapy
#25
of 40 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,380 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 323,360 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.