Are there three main subgroups within the patellofemoral pain population? A detailed characterisation study of 127 patients to help develop targeted Intervention (TIPPs)

• Background Current multimodal approaches for the management of non-specific patellofemoral pain are not optimal, however, targeted intervention for subgroups could improve patient outcomes. This study explores whether subgrouping of non-specific patellofemoral pain patients, using a series of low cost simple clinical tests, is possible. • Method The exclusivity and clinical importance of potential subgroups was assessed by applying à priori test thresholds (1 SD) from seven clinical tests in a sample of adult patients with non-specific patellofemoral pain. Hierarchical clustering and latent profile analysis, were used to gain additional insights into subgroups using data from the same clinical tests. • Results One hundred and thirty participants were recruited, 127 had complete data: 84 (66%) female, mean age 26 years (SD 5.7) and mean BMI 25.4 (SD 5.83), median (IQR) time between onset of pain and assessment was 24 (7-60) months. Potential subgroups defined by the à priori test thresholds were not mutually exclusive and patients frequently fell into multiple subgroups. Using hierarchical clustering and latent profile analysis three subgroups were identified using 6 of the 7 clinical tests. These subgroups were given the following nomenclature: (i) ‘strong’, (ii) ‘weak and tighter’, and (iii) ‘weak and pronated foot’. • Conclusions We conclude that three subgroups of patellofemoral patients may exist based on the results of six clinical tests which are feasible to perform in routine clinical practice. Further research is needed to validate these findings in other datasets and, if supported by external validation, to see if targeted interventions for these subgroups improve patient outcomes

Hierarchical Pancaking: Why the Zel'dovich Approximation Describes Coherent Large-Scale Structure in N-Body Simulations of Gravitational Clustering

To explain the rich structure of voids, clusters, sheets, and filaments apparent in the Universe, we present evidence for the convergence of the two classic approaches to gravitational clustering, the ``pancake'' and ``hierarchical'' pictures. We compare these two models by looking at agreement between individual structures -- the ``pancakes'' which are characteristic of the Zel'dovich Approximation (ZA) and also appear in hierarchical N-body simulations. We find that we can predict the orientation and position of N-body simulation objects rather well, with decreasing accuracy for increasing large-$k$ (small scale) power in the initial conditions. We examined an N-body simulation with initial power spectrum $P(k) \propto k^3$, and found that a modified version of ZA based on the smoothed initial potential worked well in this extreme hierarchical case, implying that even here very low-amplitude long waves dominate over local clumps (although we can see the beginning of the breakdown expected for $k^4$). In this case the correlation length of the initial potential is extremely small initially, but grows considerably as the simulation evolves. We show that the nonlinear gravitational potential strongly resembles the smoothed initial potential. This explains why ZA with smoothed initial conditions reproduces large-scale structure so well, and probably why our Universe has a coherent large-scale structure.Comment: 17 pages of uuencoded postscript. There are 8 figures which are too large to post here. To receive the uuencoded figures by email (or hard copies by regular mail), please send email to: [email protected]. This is a revision of a paper posted earlier now in press at MNRA

Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal

Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011–2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.info:eu-repo/semantics/publishedVersio