Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Contralateral and siblings� knees are at higher risk of ACL tear for patients with a positive history of ACL tear

Purpose: Recent studies have shown that several genetic factors can cause susceptibility to anterior cruciate ligament (ACL) rupture. The aim of the present study was to evaluate certain underlying factors that increase the risk of ACL rupture. Methods: Eight hundred thirty-six patients with ACL rupture who underwent ACL reconstructive surgery from 2010 to 2013 at an academic center completed a minimum of 5 years post-operation follow-up. The collected variables included sex, age, height, weight, exercise level, time interval between ACL rupture in the first knee and contralateral ACL rupture, dominant leg, side of the involved knee and sibling history of ACL rupture. Results: The median follow-up duration was 6.5 (range: 5�8) years. Eighty-three patients (9.9) had a contralateral ACL rupture, and 155 patients (18.5) had siblings with a history of ACL rupture. The rate of contralateral ACL rupture was three times higher in women than in men and in patients with siblings with a history of ACL rupture than in those without such history. In addition, the risk of contralateral ACL rupture was higher in those younger than 30 years of age, those with a BMI of 20�25 kg/m2 and those who participated in regular sports activity. However, whether the involved knee was on the dominant or nondominant side had no effect on the incidence of contralateral ACL rupture. The results of the study showed that 69 (83.1) of the contralateral ACL ruptures occurred within the first 2 years after the primary operation. Conclusion: In a 5- to 8-year follow-up, one out of every ten patients had a contralateral ACL rupture, and two out of every ten patients had siblings with a history of ACL rupture. The findings suggest that having a sibling with a history of ACL rupture and being female are important risk factors for ACL rupture of the contralateral knee. Level of evidence: III. © 2019, European Society of Sports Traumatology, Knee Surgery, Arthroscopy (ESSKA)

Fuzzy classification methods based diagnosis of parkinson’s disease from speech test cases

Background: Together with the Alzheimer’s disease, Parkinson’s disease is considered as one of the two serious known neurodegenerative diseases. Physicians find it hard to predict whether a given patient has already developed or is expected to develop the Parkinson’s disease in the future. To overcome this difficulty, it is possible to develop a computing model, which analyzes the data related to a given patient and predicts with acceptable accuracy when he/she is anticipated to develop the Parkinson’s disease. Objectives: This paper contributes an attractive prediction framework based on some machine learning approaches for distinguishing people with Parkinsonism from healthy individuals. Methods: Several fuzzy classifiers such as Inductive Fuzzy Classifier, Fuzzy Rough Classifier and two types of neuro-fuzzy classifiers have been employed. Results: The fuzzy classifiers utilized in this study have been tested using the “Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set” of 40 subjects available on the UCI repository. Conclusion: The results achieved show that FURIA, MLP-Bagging-SGD, genfis2 and scg1 performed the best among the fuzzy rough, WEKA, adaptive neuro-fuzzy and neuro-fuzzy classifiers, respectively. The worst performance belongs to nearest neighborhood, IBK, genfis3 and scg3 among the formerly mentioned classifiers. The results reported in this paper are better in comparison to the results reported in Sakar et al., where the same dataset was used, with utilization of different classifiers. This demonstrates the applicability and effectiveness of the fuzzy classifiers used in this study as compared to the non-fuzzy classifiers used by Sakar et al. © 2019 Bentham Science Publishers