scholarly journals Correction to: Predicting change in symptoms and function in patients with persistent shoulder pain: a prognostic model development study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mathias Moselund Rønnow ◽  
Thor André Brøndberg Stæhr ◽  
David Høyrup Christiansen
Keyword(s):  
Shoulder Pain ◽  
Prognostic Model ◽  
Model Development ◽  
Development Study ◽  
And Function

scholarly journals Predicting change in symptoms and function in patients with persistent shoulder pain: a prognostic model development study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mathias Moselund Rønnow ◽  
Thor André Brøndberg Stæhr ◽  
David Høyrup Christiansen
Keyword(s):  
Shoulder Pain ◽  
Prognostic Model ◽  
Mean Squared Error ◽  
Change Scores ◽  
Squared Error ◽  
Related Quality ◽  
Health Related ◽  
And Function

Abstract Background Persistent shoulder pain causes considerable disruption of the individual’s life and imposes high costs on healthcare and society. Well-informed treatment and referral pathways are crucial as unsuccessful interventions and longer duration of symptoms minimizes the likelihood of success in future interventions. Although physiotherapy is generally recommended as first line treatment, no prognostic model or clinical prediction rules exists to help guide the treatment of patients with persistent shoulder pain undergoing physiotherapy. Thus, the objective of this study was to develop a prognostic model to inform clinical decision making and predict change in symptoms and function in patients with persistent shoulder pain. Methods This was a prospective cohort study of 243 patients with persistent shoulder pain referred to outpatient physiotherapy rehabilitation centres. Data was collected at baseline and six-month follow-up. The outcome was change in shoulder symptoms and function as measured by the shortened version of the Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) from baseline to 6 months follow up. Potential predictors were included in a multivariable linear regression model which was pruned using modified stepwise backwards elimination. Results The final model consisted of seven predictors; baseline QuickDASH score, employment status, educational level, movement impairment classification, self-rated ability to cope with the pain, health-related quality of life and pain catastrophizing. Together these variables explained 33% of the variance in QuickDASH-change scores with a model root mean squared error of 17 points. Conclusion The final prediction model explained 33% of the variance in QuickDASH change-scores at 6 months. The root mean squared error (model SD) was relatively large meaning that the prediction of individual change scores was quite imprecise. Thus, the clinical utility of the prediction model is limited in its current form. Further work needs be done in order to improve the performance and precision of the model before external validity can be examined along with the potential impact of the model in clinical practice. Two of the included predictors were novel and could be examined in future studies; movement impairment classification based on diagnosis and health-related quality of life.

Effects of electromyographic biofeedback interventions for shoulder pain and function: Systematic review and meta-analysis

2021 ◽  
pp. 026921552199095
Author(s):  
Danilo Harudy Kamonseki ◽  
Letícia Bojikian Calixtre ◽  
Rodrigo Py Gonçalves Barreto ◽  
Paula Rezende Camargo
Keyword(s):  
Systematic Review ◽  
Clinical Trials ◽  
Confidence Interval ◽  
Shoulder Pain ◽  
Meta Analysis ◽  
Quality Of Evidence ◽  
Electromyographic Biofeedback ◽  
Mean Differences ◽  
And Function

Objective: To systematically review the effectiveness of electromyographic biofeedback interventions to improve pain and function of patients with shoulder pain. Design: Systematic review of controlled clinical trials. Literature search: Databases (Medline, EMBASE, CINAHL, PEDro, CENTRAL, Web of Science, and SCOPUS) were searched in December 2020. Study selection criteria: Randomized clinical trials that investigated the effects of electromyographic biofeedback for individuals with shoulder pain. Patient-reported pain and functional outcomes were collected and synthesized. Data synthesis: The level of evidence was synthesized using GRADE and Standardized Mean Differences and 95% confidence interval were calculated using a random-effects inverse variance model for meta-analysis. Results: Five studies were included with a total sample of 272 individuals with shoulder pain. Very-low quality of evidence indicated that electromyographic biofeedback was not superior to control for reducing shoulder pain (standardized mean differences = −0.21, 95% confidence interval: −0.67 to 0.24, P = 0.36). Very-low quality of evidence indicated that electromyographic biofeedback interventions were not superior to control for improving shoulder function (standardized mean differences = −0.11, 95% confidence interval: −0.41 to 0.19, P = 0.48). Conclusion: Electromyographic biofeedback may be not effective for improving shoulder pain and function. However, the limited number of included studies and very low quality of evidence does not support a definitive recommendation about the effectiveness of electromyographic biofeedback to treat individuals with shoulder pain.

Machine learning in prognosis research

2019 ◽  
pp. 328-342
Author(s):  
Mihaela van der Schaar ◽  
Harry Hemingway
Keyword(s):  
Machine Learning ◽  
Health Outcomes ◽  
Prognostic Model ◽  
Dynamic Models ◽  
Model Development ◽  
Data Driven ◽  
Complex Data ◽  
Pragmatic Approach ◽  
Prognosis Research ◽  
Research Types

Machine learning offers an alternative to the methods for prognosis research in large and complex datasets and for delivering dynamic models of prognosis. Machine learning foregrounds the capacity to learn from large and complex data about the pathways, predictors, and trajectories of health outcomes in individuals. This reflects wider societal drives for data-driven modelling embedded and automated within powerful computers to analyse large amounts of data. Machine learning derives algorithms that can learn from data and can allow the data full freedom, for example, to follow a pragmatic approach in developing a prognostic model. Rather than choosing factors for model development in advance, machine learning allows the data to reveal which features are important for which predictions. This chapter introduces key machine learning concepts relevant to each of the four prognosis research types, explains where it may enhance prognosis research, and highlights challenges.

Shoulder Numbness in a Patient with Suprascapular Nerve Entrapment Syndrome: Cutaneous Branch of the Suprascapular Nerve: Case Report

Neurosurgery ◽  
2000 ◽  
Vol 47 (6) ◽  
pp. 1452-1452 ◽  
Author(s):  
Kimberly S. Harbaugh ◽  
Rand Swenson ◽  
Richard L. Saunders
Keyword(s):  
Shoulder Pain ◽  
Suprascapular Nerve ◽  
Suprascapular Notch ◽  
Study Results ◽  
Cutaneous Branch ◽  
Shoulder Region ◽  
Motor Abnormalities ◽  
The Right ◽  
And Function ◽  
Nerve Entrapment Syndrome

ABSTRACT OBJECTIVE AND IMPORTANCE The ability to diagnose peripheral nerve disorders is dependent on knowledge of the anatomic course and function of the nerves in question. The classic teaching regarding the suprascapular nerve (SScN) is that it has no cutaneous branches, despite the fact that a cutaneous branch was first reported in the anatomic literature 20 years ago. CLINICAL PRESENTATION We describe a case of a 35-year-old male patient who presented with right shoulder pain and atrophy and weakness of the right supra- and infraspinatus muscles. During the examination, he was also noted to have an area of numbness involving the right upper lateral shoulder region. Electrical study results were consistent with SScN entrapment at the suprascapular notch. INTERVENTION The patient underwent surgical decompression 7 months after the onset of his symptoms. The patient noted resolution of his shoulder pain immediately after the procedure, and his shoulder sensory disturbance had improved by 2 weeks. At 9 months after surgery, he remained pain-free, his shoulder sensation was normal, and his motor abnormalities had improved significantly. CONCLUSION This case provides clinical evidence for the presence of a cutaneous branch of the SScN, as described in cadaveric studies. Although shoulder numbness demands a search for alternative diagnoses, it does not necessarily exclude the diagnosis of SScN entrapment.

Electromechanical Actuator Test Stand Coupling Design to Support Actuator Prognostic Model Development

2011 ◽  
Author(s):  
Michael T. Koopmans ◽  
Irem Y. Tumer
Keyword(s):  
Prognostic Model ◽  
Model Development ◽  
Uncertainty Assessment ◽  
Test Stand ◽  
System Level ◽  
Data Sets ◽  
Component Level ◽  
Electromechanical Actuator ◽  
Response Data ◽  
Failure Data

Uncertainty assessment and management is becoming an increasingly essential aspect of good prognostic design for engineering complex systems. Uncertainty surrounding diagnostics, loads, and fault progression models is very real and propagating this uncertainty from component-level health estimates to the system-level remains difficult at best. In this work, a test stand is used to conduct real-time failure experiments aboard various aircraft platforms to collect failure response data, expanding the actuator knowledge base that forms the foundation of component health estimations. The research takes a step towards standardizing a test stand design to produce comparable and scalable failure data sets, fostering uncertainty reduction within the electromechanical actuator prognostic model. This paper specifically presents a method to optimize the actuator coupling for a commercially available actuator where a model was built to minimize the coupling deflection and estimate the coupling life. Using this model, researchers can rapidly develop their own electromechanical actuator test stands.

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