Additional file 1: of Poor return on investment: investigating the barriers that cause low credentialing yields in a resource-limited clinical ultrasound training programme

Survey questionnaire. (DOCX 91 kb

Additional file 1: of Accuracy of acute burns diagnosis made using smartphones and tablets: a questionnaire-based study among medical experts

Raw data for Table S4 and S6. The file contains raw data for Tables S4 and S6 with assessments of TBSA and depth for each image and participant. Participant 1 is the bedside diagnosis which is used as the gold standard for all analyses. Participant 1 was not used for reliability analyses. (XLSX 42 kb

The Notion of State Power: Etymology Basis of Research

На сучасному етапі реформування української державності, побудови в Україні правової держави важливим є встановлення сутності держави та державної влади, визначення ролі, яку відіграє кожна з її гілок у цих процесах.At the current stage of reforming Ukrainian statehood, building a legal one in Ukraine States are important to establish the essence of state and state power, determining the role of which plays each of its branches in these processes

MOESM2 of Developing prehospital clinical practice guidelines for resource limited settings: why re-invent the wheel?

Additional file 2. Suitcase analogy for adopting, adapting and contextualising guidelines

MOESM4 of Developing prehospital clinical practice guidelines for resource limited settings: why re-invent the wheel?

Additional file 4. De novo vs African Federation for Emergency Medicine (AFEM) alternative guideline development approach

Relative feature importance plot.

COVID-19 infection rates remain high in South Africa. Clinical prediction models may be helpful for rapid triage, and supporting clinical decision making, for patients with suspected COVID-19 infection. The Western Cape, South Africa, has integrated electronic health care data facilitating large-scale linked routine datasets. The aim of this study was to develop a machine learning model to predict adverse outcome in patients presenting with suspected COVID-19 suitable for use in a middle-income setting. A retrospective cohort study was conducted using linked, routine data, from patients presenting with suspected COVID-19 infection to public-sector emergency departments (EDs) in the Western Cape, South Africa between 27th August 2020 and 31st October 2021. The primary outcome was death or critical care admission at 30 days. An XGBoost machine learning model was trained and internally tested using split-sample validation. External validation was performed in 3 test cohorts: Western Cape patients presenting during the Omicron COVID-19 wave, a UK cohort during the ancestral COVID-19 wave, and a Sudanese cohort during ancestral and Eta waves. A total of 282,051 cases were included in a complete case training dataset. The prevalence of 30-day adverse outcome was 4.0%. The most important features for predicting adverse outcome were the requirement for supplemental oxygen, peripheral oxygen saturations, level of consciousness and age. Internal validation using split-sample test data revealed excellent discrimination (C-statistic 0.91, 95% CI 0.90 to 0.91) and calibration (CITL of 1.05). The model achieved C-statistics of 0.84 (95% CI 0.84 to 0.85), 0.72 (95% CI 0.71 to 0.73), and 0.62, (95% CI 0.59 to 0.65) in the Omicron, UK, and Sudanese test cohorts. Results were materially unchanged in sensitivity analyses examining missing data. An XGBoost machine learning model achieved good discrimination and calibration in prediction of adverse outcome in patients presenting with suspected COVID19 to Western Cape EDs. Performance was reduced in temporal and geographical external validation.</div

Manželské mýty

<p>Schematic of the referral pathway for trauma patients admitted to Paediatric Intensive Care.</p

Diagnostic accuracy at different base case model thresholds in thresholds in Western Cape Omicron wave test data.

Diagnostic accuracy at different base case model thresholds in thresholds in Western Cape Omicron wave test data.</p

Diagnostic accuracy at different base case model thresholds in PRIEST test data.

Diagnostic accuracy at different base case model thresholds in PRIEST test data.</p

Missing data secondary analysis—Single deterministic imputation.

Calibration plots (bottom panel) and receiver operating characteristic curves (top panel) for test cohorts. ROC curves are labelled with 10 representative probability thresholds. (TIF)</p