ICON 2019: International Scientific Tendinopathy Symposium Consensus: Clinical Terminology

© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.Background Persistent tendon pain that impairs function has inconsistent medical terms that can influence choice of treatment.1 When a person is told they have tendinopathy by clinician A or tendinitis by clinician B, they might feel confused or be alarmed at receiving what they might perceive as two different diagnoses. This may lead to loss of confidence in their health professional and likely adds to uncertainty if they were to search for information about their condition. Clear and uniform terminology also assists inter-professional communication. Inconsistency in terminology for painful tendon disorders is a problem at numerous anatomical sites. Historically, the term ‘tendinitis’ was first used to describe tendon pain, thickening and impaired function (online supplementary figure S1). The term ‘tendinosis’ has also been used in a small number of publications, some of which were very influential.2 3 Subsequently, ‘tendinopathy’ emerged as the most common term for persistent tendon pain.4 5 To our knowledge, experts (clinicians and researchers) or patients have never engaged in a formal process to discuss the terminology we use. We believe that health professionals have not yet agreed on the appropriate terminology for painful tendon conditions.Peer reviewedFinal Accepted Versio

ICON 2019—International Scientific Tendinopathy Symposium Consensus: There are nine core health-related domains for tendinopathy (CORE DOMAINS): Delphi study of healthcare professionals and patients

Background: The absence of any agreed-upon tendon health-related domains hampers advances in clinical tendinopathy research. This void means that researchers report a very wide range of outcome measures inconsistently. As a result, substantial synthesis/meta-analysis of tendon research findings is almost futile despite researchers publishing busily. We aimed to determine options for, and then define, core health-related domains for tendinopathy. Methods: We conducted a Delphi study of healthcare professionals (HCP) and patients in a three-stage process. In stage 1, we extracted candidate domains from clinical trial reports and developed an online survey. Survey items took the form: ‘The ‘candidate domain’ is important enough to be included as a core health-related domain of tendinopathy’; response options were: agree, disagree, or unsure. In stage 2, we administered the online survey and reported the findings. Stage 3 consisted of discussions of the findings of the survey at the ICON (International Scientific Tendinopathy Symposium Consensus) meeting. We set 70% participant agreement as the level required for a domain to be considered ‘core’; similarly, 70% agreement was required for a domain to be relegated to ‘not core’ (see Results next). Results: Twenty-eight HCP (92% of whom had >10 years of tendinopathy experience, 71% consulted >10 cases per month) and 32 patients completed the online survey. Fifteen HCP and two patients attended the consensus meeting. Of an original set of 24 candidate domains, the ICON group deemed nine domains to be core. These were: (1) patient rating of condition, (2) participation in life activities (day to day, work, sport), (3) pain on activity/loading, (4) function, (5) psychological factors, (6) physical function capacity, (7) disability, (8) quality of life and (9) pain over a specified time. Two of these (2, 6) were an amalgamation of five candidate domains. We agreed that seven other candidate domains were not core domains: range of motion, pain on clinician applied test, clinical examination, palpation, drop out, sensory modality pain and pain without other specification. We were undecided on the other five candidate domains of physical activity, structure, medication use, adverse effects and economic impact. Conclusion: Nine core domains for tendon research should guide reporting of outcomes in clinical trials. Further research should determine the best outcome measures for each specific tendinopathy (ie, core outcome sets)

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification.</p> <p>Results</p> <p>In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model.</p> <p>Conclusions</p> <p>FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial species. Summarized, by phylogenetic learning we are able to situate and evaluate FAME-based bacterial species classification in a more informative context.</p

Association between depression, anxiety and weight change in young adults

Background To investigate whether there are bi-directional associations between anxiety and mood disorders and body mass index (BMI) in a cohort of young adults. Methods We analysed data from the 2004–2006 (baseline) and 2009–2011 (follow-up) waves of the Childhood Determinants of Adult Health study. Lifetime DSM-IV anxiety and mood disorders were retrospectively diagnosed with the Composite International Diagnostic Interview. Potential mediators were individually added to the base models to assess their potential role as a mediator of the associations. Results In males, presence of mood disorder history at baseline was positively associated with BMI gain (β = 0.77, 95% CI: 0.14–1.40), but baseline BMI was not associated with subsequent risk of mood disorder. Further adjustment for covariates, including dietary pattern, physical activity, and smoking reduced the coefficient (β) to 0.70 (95% CI: 0.01–1.39), suggesting that the increase in BMI was partly mediated by these factors. In females, presence of mood disorder history at baseline was not associated with subsequent weight gain, however, BMI at baseline was associated with higher risk of episode of mood disorder (RR per kg/m2: 1.04, 95% CI: 1.01–1.08), which was strengthened (RR per kg/m2 = 1.07, 95% CI: 1.00–1.15) after additional adjustment in the full model. There was no significant association between anxiety and change in BMI and vice-versa. Conclusion The results do not suggest bidirectional associations between anxiety and mood disorders, and change in BMI. Interventions promoting healthy lifestyle could contribute to reducing increase in BMI associated with mood disorder in males, and excess risk of mood disorder associated with BMI in females

The CLIC Potential for New Physics

The Compact Linear Collider (CLIC) is a mature option for the future of high energy physics. It combines the benefits of the clean environment of $e^+e^-$ colliders with operation at high centre-of-mass energies, allowing to probe scales beyond the reach of the Large Hadron Collider (LHC) for many scenarios of new physics. This places the CLIC project at a privileged spot in between the precision and energy frontiers, with capabilities that will significantly extend knowledge on both fronts at the end of the LHC era. In this report we review and revisit the potential of CLIC to search, directly and indirectly, for physics beyond the Standard Model

10 Recommended core outcome domains for tendinopathy derived from a delphi of patients and health care professionals: the groningen ISTS2018 consensus

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