Development of a trail running injury screening instrument: A multiple methods approach

ObjectiveTo develop a trail running injury screening instrument (TRISI) for utilisation as clinical decision aid in determining if a trail runner is at an increased risk for injury.DesignMultiple methods approach.MethodsThe study utilised five phases 1) identification of injury risk factors 2) determining the relevance of each identified risk factor in a trail running context, 3) creating the content of the Likert scale points from 0 to 4, 4) rescaling the Likert scale points to determine numerical values for the content of each Likert scale point, and 5) determining a weighted score for each injury risk factor that contributes to the overall combined composite score.ResultsOf the 77 identified injury risk factors, 26 were deemed relevant in trail running. The weighted score for each injury risk factor ranged from 2.21 to 5.53 with the highest calculated score being 5.53. The final TRISI includes risk categories of training, running equipment, demographics, previous injury, behavioural, psychological, nutrition, chronic disease, physiological, and biomechanical factors.ConclusionThe developed TRISI aims to assist the clinician during pre-race injury screening or during a training season to identify meaningful areas to target in designing injury risk management strategies and/or continuous health education.     

医院档案管理工作创新方法初探

档案管理是医院管理中一项重要的内容。医院传统档案管理已经不能满足现代化社会发展的要求,基于此需要加强对医院档案管理的创新,只有在加快医院档案管理创新的基础上才能提高档案管理的效率和水平,并推动社会的进一步发展,可见创新医院档案管理方法是时代发展的必然选择。该文主要针对现阶段创新医院档案管理工作方法的相关问题进行研究,目的是为医院档案管理纳入新的工作方法,提高医院管理水平。     

上海市普通外科病人静脉血栓栓塞症防治管理规范(2020版)

静脉血栓栓塞症(VTE)是指各种病因(如静脉血滞缓、静脉壁血管内膜损伤或血液高凝等)导致血液在静脉系统不正常凝集的一组疾病,包括肺动脉栓塞(PE)和深静脉血栓形成(DVT)。上海市普通外科临床质量控制中心自2018年发布《上海市普通外科住院病人静脉血栓栓塞症防治管理规范》(以下简称2018版《规范》)至今已2年,推动了上海市普通外科围手术期血栓防治工作的推广,同时积累了上海在血栓防治方面的临床数据。截止至2019年9月,上海市二级以上医院已有88%建立VTE防控体系,在病史中包含了VTE风险评估表,并且大部分医院已有电子病案的嵌入或纸质病史的记录;84%的医院有VTE和PE的联合会诊团队;92%医院的普通外科已按照2018版《规范》为住院病人评估VTE风险,74%的科室在病人手术后再次评估VTE风险,38%的科室在出院前会再次为病人评估VTE风险。上海市普通外科在临床实践中防治VTE的理念和实践有了很大进步,但与先进国家和医院的防治实践相比仍有较大差距。     

Factors associated with clinically significant hypoglycemia in patients with type 1 diabetes using sensor-augmented pump therapy with predictive low-glucose management: A multicentric study on iberoamerica

Background and aimsDespite using sensor-augmented pump therapy (SAPT) with predictive low-glucose management (PLGM), hypoglycemia is still an issue in patients with type 1 Diabetes (T1D). Our aim was to determine factors associated with clinically significant hypoglycemia (<54 mg/dl) in persons with T1D treated with PLGM-SAPT.Methodology: This is a multicentric prospective real-life study performed in Colombia, Chile and Spain. Patients with T1D treated with PLGM-SAPT, using sensor ≥70% of time, were included. Data regarding pump and sensor use patterns and carbohydrate intake from 28 consecutive days were collected. A bivariate and multivariate Poisson regression analysis was carried out, to evaluate the association between the number of events of <54 mg/dl with the clinical variables and patterns of sensor and pump use.Results188 subjects were included (41 ± 13.8 years-old, 23 ± 12 years disease duration, A1c 7.2% ± 0.9). The median of events <54 mg/dl was four events/patient/month (IQR 1–10), 77% of these events occurred during day time. Multivariate analysis showed that the number of events of hypoglycemia were higher in patients with previous severe hypoglycemia (IRR1.38; 95% CI 1.19–1.61; p < 0.001), high glycemic variability defined as Coefficient of Variation (CV%) > 36% (IRR 2.09; 95%CI 1.79–2.45; p < 0.001) and hypoglycemia unawareness. A protector effect was identified for adequate sensor calibration (IRR 0.77; 95%CI 0.66–0.90; p:0.001), and the use of bolus wizard >60% (IRR 0.74; 95%CI 0.58–0.95; p:0.017).ConclusionIn spite of using advanced SAPT, clinically significant hypoglycemia is still a non-negligible risk. Only the identification and intervention of modifiable factors could help to prevent and reduce hypoglycemia in clinical practice.     

Symptomatic palliative care for children with neurodisability

Paediatric palliative care and neurodisability are two relatively new, evolving paediatric sub-specialities that have increasing relevance in the current paediatric landscape. For many people palliative care has been synonymous with end of life care, but in paediatrics it encompasses much more and is for all children with life-threatening or life-limiting conditions, from the point of diagnosis. This breadth of focus is demonstrated well through the interface between paediatric palliative care and paediatric neurodisability. In this article we explore this unique interface through the three domains of complex symptom management, advanced care planning and end of life care. We describe the practicalities involved in all three areas and highlight the importance of early referral and the process of “dual” or “parallel” planning. We cover in more depth the specific management of the symptoms: dystonia/abnormalities of muscle tone, seizures, pain, agitation, secretions, respiratory failure, and gut failure.     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.