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

Abstract 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

Histiocitosis de células de Langerhans localizada en hueso malar. Presentación de un caso

ResumenLa histiocitosis de células de Langerhans localizada (HCLL), conocida como granuloma eosinófilo, representa entre el 50 y el 60% de todos los casos de histiocitosis de células de Langerhans. El tratamiento clásico para la HCLL ha sido el curetaje o la resección de las lesiones óseas. Hay publicaciones de casos tratados con inyección intralesional de corticosteroides, combinado con curetaje.Se presenta un caso clínico de un paciente de tres años de edad con diagnóstico de HCLL que compromete en su extensión el hueso malar, tratado con infiltraciones de corticosteroides y posterior curetaje de la lesión. A un año de realizado el tratamiento, el paciente se encuentra asintomático y con una regeneración ósea del hueso malar, evidenciable en la tomografía axial computarizada.AbstractLocalized Langerhans cell histiocytosis (LLCH), also known as eosinophilic granuloma, represents 50 to 60% of all cases of Langerhans cell histiocytosis. The standard treatment for LLCH has been lesion curettage or resection. Cases treated with intralesional corticosteroid injections combined with curettage have been described.We report the case of a three-year-old patient diagnosed of LLCH with extensive zygomatic bone involvement, who was treated with corticosteroid infiltrations and subsequent curettage of the lesion. One year after treatment, the patient is asymptomatic with zygomatic reossification evidenced on computed tomography

Discovering HIV related information by means of association rules and machine learning

Acquired immunodeficiency syndrome (AIDS) is still one of the main health problems worldwide. It is therefore essential to keep making progress in improving the prognosis and quality of life of affected patients. One way to advance along this pathway is to uncover connections between other disorders associated with HIV/AIDS-so that they can be anticipated and possibly mitigated. We propose to achieve this by using Association Rules (ARs). They allow us to represent the dependencies between a number of diseases and other specific diseases. However, classical techniques systematically generate every AR meeting some minimal conditions on data frequency, hence generating a vast amount of uninteresting ARs, which need to be filtered out. The lack of manually annotated ARs has favored unsupervised filtering, even though they produce limited results. In this paper, we propose a semi-supervised system, able to identify relevant ARs among HIV-related diseases with a minimal amount of annotated training data. Our system has been able to extract a good number of relationships between HIV-related diseases that have been previously detected in the literature but are scattered and are often little known. Furthermore, a number of plausible new relationships have shown up which deserve further investigation by qualified medical experts