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This case revolves around a mid-childhood boy diagnosed with a chemoresistant chondroblastic osteosarcoma, a rare and aggressive form of bone tumour affecting his left proximal humerus. Histopathological confirmation of chondroblastic osteosarcoma was obtained through core-needle biopsy. Despite initiating cytoreductive neoadjuvant chemotherapy using a vincristine and cyclophosphamide regimen, the tumour exhibited resistance, prompting the decision to proceed with a forequarter amputation.

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Aim: To explore different types of interprofessional education (IPE) teaching strategies used in pre-licensure interprofessional learning programmes and the effective components of these strategies in promoting student learning, IPE skills, behavioural change, organisational practice, or patient health outcomes.

Background: IPE is rapidly becoming a core element of health professions preparation programmes worldwide, but the differential effects of different ways of delivering IPE are not well documented.

Design: Systematic narrative review.

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Incidence of adverse outcome events rises as patients with advanced illness approach end-of-life. Exposures that tend to occur near end-of-life, for example, use of wheelchair, oxygen therapy and palliative care, may therefore be found associated with the incidence of the adverse outcomes. We propose a concept of reverse time-to-death (rTTD) and its use for the time-scale in time-to-event analysis based on partial likelihood to mitigate the time-varying confounding.

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Aims: An explainable advanced electrocardiography (A-ECG) Heart Age gap is the difference between A-ECG Heart Age and chronological age. This gap is an estimate of accelerated cardiovascular aging expressed in years of healthy human aging, and can intuitively communicate cardiovascular risk to the general population. However, existing A-ECG Heart Age requires sinus rhythm.

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Context: Accurate prognosis prediction for cancer patients in palliative care is critical for clinical decision-making and personalized care. Traditional statistical models have been complemented by machine learning approaches; however, their comparative effectiveness remains underexplored.

Objectives: To assess the prognostic accuracy of statistical and machine learning models in predicting 30-day survival in patients with advanced cancer using objective data, such as the result of the blood test.

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