68 research outputs found

    Aggressive, Metastatic Squamous Cell Carcinoma After a 46-Year-Old History of Chronic Osteomyelitis and Local Infectious Complications: A Case Report

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    Marjolin's ulcer is a squamous cell carcinoma (SCC) arising from any site of established chronic inflammation, especially with presence of scar tissue.An emblematic case report of locally disseminated SCC arising from a chronic osteomyelitis of the left leg complicated by recurring soft tissue infections lasting since 46 years is presented and discussed according to the available international literature evidence. Concurrent diseases, supporting factors, clinical and histopathological presentation, differential diagnosis, and time and mode of management of this potentially functional- and life-threatening pathological condition are reviewed and discussed to offer a theme to daily clinical care

    Berseem-annual ryegrass intercropping: effect of plant arrangement and seeding ratio on N2 fixation and yield

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    Various agronomic factors can affect the productivity and the efficiency of legume-grass intercropping systems. This research was carried out in a Mediterranean semi-arid environment (37°30’N; 13°31’E; 178 m a.s.l.) with the aim to study on berseem clover- annual ryegrass mixture (Trifolium alexandrinum L. – Lolium multiflorum Lam subsp. wersterwoldicum) the effects of different plant arrangement (sowing of the two components in alternate rows or in the same row) and seeding ratios (100:0; 75:25; 50:50; 25:75; 0:100) on forage yield, nitrogen content and nitrogen fixation. The experimental design was a split-plot with four replications. The 15N isotope dilution technique was used to estimate nitrogen fixation by berseem clover. All plots were cut four times (first cut 85 DAS; rest period of four weeks). The DM yield of mixtures and pure stand clover were similar; annual ryegrass in pure stand produced the lowest yields. No significant differences for DM yield were observed due to the plant arrangement and seeding ratio in the mixture. Intercropping berseem had always a significant higher percentage of Ndfa than monocropped berseem. The %Ndfa of mixed berseem was not influenced by plant arrangement but gradually decreased when proportion of berseem in the mixture increased

    Grading of recommendations, assessment, development and evaluations concept 7: issues and insights linking guideline recommendations to trustworthy essential medicine lists

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    Objectives: Guidelines and essential medicine lists (EMLs) bear similarities and differences in the process that lead to decisions. Access to essential medicines is central to achieve universal health coverage. The World Health Organization (WHO) EML has guided prioritization of essential medicines globally for nearly 50 years, and national EMLs (NEMLs) exist in over 130 countries. Guideline and EML decisions, at WHO or national levels, are not always coordinated and aligned. We sought to explore challenges, and potential solutions, for decision-making to support trustworthy medicine selection for EMLs from a Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Working Group perspective. We primarily focus on the WHO EML; however, our findings may be applicable to NEML decisions as well. Study Design and Setting: We identified key challenges in connecting the EML to health guidelines by involving a broad group of stakeholders and assessing case studies including real applications to the WHO EML, South Africa NEML, and a multiple sclerosis guideline connected to a WHO EML application for multiple sclerosis treatments. To address challenges, we utilized the results of a survey and feedback from the stakeholders, and iteratively met as a project group. We drafted a conceptual framework of challenges and potential solutions. We presented a summary of the results for feedback to all attendees of the GRADE Working Group meetings in November 2022 (approximately 120 people) and in May 2023 (approximately 100 people) before finalizing the framework. Results: We prioritized issues and insights/solutions that addressed the connections between the EML and health guidelines. Our suggested solutions include early planning alignment of guideline groups and EMLs, considering shared participation to strengthen linkage, further clarity on price/cost considerations, and using explicit shared criteria to make guideline and EML decisions. We also provide recommendations to strengthen the connection between WHO EML and NEMLs including through contextualization methods. Conclusion: This GRADE concept article, jointly developed by key stakeholders from the guidelines and EMLs field, identified key conceptual issues and potential solutions to support the continued advancement of trustworthy EMLs. Adopting structured decision criteria that can be linked to guideline recommendations bears the potential to advance health equity and gaps in availability of essential medicines within and between countries

    Quality indicators for patients with traumatic brain injury in European intensive care units

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    Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

    Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

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    Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations
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