Tendon–bone contact pressure and biomechanical evaluation of a modified suture-bridge technique for rotator cuff repair

The aim of the study was to evaluate the time-zero mechanical and footprint properties of a suture-bridge technique for rotator cuff repair in an animal model. Thirty fresh-frozen sheep shoulders were randomly assigned among three investigation groups: (1) cyclic loading, (2) load-to-failure testing, and (3) tendon–bone interface contact pressure measurement. Shoulders were cyclically loaded from 10 to 180 N and displacement to gap formation of 5- and 10-mm at the repair site. Cycles to failure were determined. Additionally, the ultimate tensile strength and stiffness were verified along with the mode of failure. The average contact pressure and pressure pattern were investigated using a pressure-sensitive film system. All of the specimens resisted against 3,000 cycles and none of them reached a gap formation of 10 mm. The number of cycles to 5-mm gap formation was 2,884.5 ± 96.8 cycles. The ultimate tensile strength was 565.8 ± 17.8 N and stiffness was 173.7 ± 9.9 N/mm. The entire specimen presented a unique mode of failure as it is well known in using high strength sutures by pulling them through the tendon. We observed a mean contact pressure of 1.19 ± 0.03 MPa, applied on the footprint area. The fundamental results of our study support the use of a suture-bridge technique for optimising the conditions of the healing biology of a reconstructed rotator cuff tendon. Nevertheless, an individual estimation has to be done if using the suture-bridge technique clinically. Further investigation is necessary to evaluate the cell biological healing process in order to achieve further sufficient advancements in rotator cuff repair

Sentence-Based Text Analysis for Customer Reviews

Firms collect an increasing amount of consumer feedback in the form of unstructured consumer reviews. These reviews contain text about consumer experiences with products and services that are different from surveys that query consumers for specific information. A challenge in analyzing unstructured consumer reviews is in making sense of the topics that are expressed in the words used to describe these experiences. We propose a new model for text analysis that makes use of the sentence structure contained in the reviews and show that it leads to improved inference and prediction of consumer ratings relative to existing models using data from www.expedia.com and www.we8there.com. Sentence-based topics are found to be more distinguished and coherent than those identified from a word-based analysis

Improving Text Analysis Using Sentence Conjunctions and Punctuation

User generated content in the form of customer reviews, blogs or tweets is an emerging and rich source of data for marketers. Topic models have been successfully applied to such data, demonstrating that empirical text analysis benefits greatly from a latent variable approach which summarizes high-level interactions among words. We propose a new topic model that allows for serial dependency of topics in text. That is, topics may carry over from word to word in a document, violating the bag-of-words assumption in traditional topic models. In our model, topic carry-over is informed by sentence conjunctions and punctuation. Typically, such observed information is eliminated prior to analyzing text data (i.e., “pre-processing”) because words such as “and” and “but” do not differentiate topics. We find that these elements of grammar contain information relevant to topic changes. We examine the performance of our model using multiple data sets and estab- lish boundary conditions for when our model leads to improved inference about customer evaluations. Implications and opportunities for future research are discussed

The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis

The canonical design of customer satisfaction surveys asks for global satisfaction with a product or service and for evaluations of its distinct attributes. Users of these surveys are often interested in the relationship between global satisfaction and attributes; regression analysis is commonly used to measure the conditional associations. Regression analysis is only appropriate when the global satisfaction measure results from the attribute evaluations and is not appropriate when the covariance of the items lie in a low-dimensional subspace, such as in a factor model. Potential reasons for low-dimensional responses are that responses may be haloed from overall satisfaction and there may be an unintended lack of item specificity. In this paper we develop a Bayesian mixture model that facilitates the empirical distinction between regression models and relatively much lower-dimensional factor models. The model uses the dimensionality of the covariance among items in a survey as the primary classification criterion while accounting for the heterogeneous usage of rating scales. We apply the model to four different customer satisfaction surveys that evaluate hospitals, an academic program, smartphones, and theme parks, respectively. We show that correctly assessing the heterogeneous dimensionality of responses is critical for meaningful inferences by comparing our results to those from regression models

Explaining Preference Heterogeneity with Mixed Membership Modeling

Choice models produce part-worth estimates that tell us what product attributes individuals prefer. However, to understand the drivers of these preferences we need to model consumer heterogeneity by specifying covariates that explain cross-sectional variation in the part-worths. In this paper we demonstrate a way to generate covariates for the upper level of a hierarchical Bayesian choice model that leads to an improvement in explaining preference heterogeneity. The covariates are uncovered by augmenting the choice model with a grade of membership model. We find improvement in model fit and inference using the covariates generated with the proposed integrated model over competing models. This paper provides an important step in both a proper accounting for extremes in preference heterogeneity and a continued synthesis between marketing models and mixed membership models, which include models for text data