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Background Methods Results Conclusion Climate change is a critical global issue, impacting ecosystems, economies and communities worldwide. The shift from paper‐based to digital systems is becoming increasingly prevalent across industries, with downstream positive impacts on sustainability. In 2020, Austin Health, a public tertiary hospital in Victoria, Australia, adopted a web‐based outpatient booking application, which succeeded the prior paper‐based system. The application served as an integrated platform for administrative staff to access various Austin Information Technology platforms and replaced previous mail‐based outpatient appointment notifications with Short Message Service‐based notifications. This study aimed to assess the environmental impact and organisation‐wide economic cost of a web‐based outpatient booking application compared to the prior paper‐based system across the same time period.A retrospective environmental and economic assessment was conducted for both the web‐based booking application and the paper‐based system. The evaluation covered 36,925 outpatient diagnostic imaging studies – including CT, MRI, ultrasound and mammography – performed at Austin Health from 1st July 2023 until 30th June 2024. The environmental impact was assessed by calculating the expected carbon dioxide equivalent (CO2e) emissions produced by each system. The economic cost analysis was conducted from the perspective of the hospital and included the direct costs of labour and materials/consumables.CO2e emissions were significantly reduced using the web‐based outpatient booking application compared to the prior paper‐based system (38.5 tonnes compared to 0.002 tonnes), predominantly attributable to the elimination of postage‐related fuel emissions (27.7 tonnes). The estimated net operating cost savings across the year was at least AUD 175,000 (in 2024 Australian Dollars, adjusted for inflation). This was primarily due to labour savings from the elimination of workflow inefficiencies (at least 2342 h saved) amounting to at least AUD 85,272 in salary, followed by savings from eliminated pathology tests (AUD 57,422) and postage costs (AUD 55,193).The adoption of the web‐based outpatient booking application led to a substantial reduction in carbon emissions and operating costs, alongside enhanced operational efficiency and productivity. These benefits are anticipated to be enduring, especially in the face of an ever‐increasing demand for medical imaging services. [ABSTRACT FROM AUTHOR]

Objectives: Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data. Methods: A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature. Results: Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56). Conclusions: This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting. Advances in knowledge: This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status. [ABSTRACT FROM AUTHOR]

Desmoid tumours are benign but locally aggressive mesenchymal neoplasms that occur most commonly in the abdomen, with the potential to invade surrounding structures causing significant morbidity. Lateral abdominal wall defects are known to be more challenging and less frequently encountered compared to ventral abdominal wall defects. Asymmetric forces caused by contraction of remnant rectus and contralateral oblique muscles increase the risk of herniation postoperatively. We report a case of a challenging abdominal wall reconstruction after desmoid tumour resection in a 62-year-old male patient who presented to our hospital with a progressively enlarging left upper back lump of 6 months duration. A venous supercharged pedicled anterolateral thigh flap was combined with PROLENE® mesh for reconstruction, and the patient recovered well with good functional and aesthetic outcomes at 2-year follow-up. The pedicled anterolateral thigh flap with venous supercharging can be effectively used for the reconstruction of extensive lateral abdominal wall defects. [ABSTRACT FROM AUTHOR]

This study aimed at investigating the impact of entering high-ability classes on the academic self-concept of high-ability primary girls in Singapore. Participants in this study are 91 Primary 4 girls, 30 high-ability pupils, and 61 pupils from classes that include high-, middle-, and low-ability pupils. This study utilized a mixed-method approach. The quantitative part of the study used the Academic Self-Concept Questionnaire (ASCQ) to measure the pupils' academic self-concept before and after they were streamed into high-ability classes. Findings indicate that the high-ability learners had a statistically lower academic self-concept score after streaming, whereas no significant difference could be found between the academic self- concept scores for the pupils from the mixed-ability classes before and after streaming had taken place. Narrative interviews revealed that high-ability students experienced greater pressure because of heightened competition in class. Implications of the study for educators are discussed.

Background Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention. Methods We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43xx1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. Results ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. Conclusions ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use. [ABSTRACT FROM AUTHOR]

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting. [ABSTRACT FROM AUTHOR]

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Background: Minimally invasive pancreaticoduodenectomy (PD) is a feasible option for periampullary tumours. However, it remains a complex procedure with no proven advantages over open PD (OPD). The aim of the study was to compare the outcomes between laparoscopic‐assisted PD (LAPD) and OPD using a propensity score‐matched analysis. Methods: Retrospective review of 40 patients who underwent PD for periampullary tumours between January 2014 and December 2016 was conducted. The patients were matched 1:1 for age, gender, body mass index, Charlson comorbidty index, tumour size and haematological indices. Peri‐operative outcomes were evaluated. Results: LAPD appeared to have a longer median operative time as compared to OPD (LAPD, 425 min (285–597) versus OPD, 369 min (260–500)) (P = 0.066). Intra‐operative blood loss was comparable between both groups. Respiratory complications were five times higher in the OPD group (LAPD, 5% versus OPD, 25%) (P = 0.077), while LAPD patients required less time to start ambulating post‐operatively (LAPD, 2 days versus OPD, 2 days) (P = 0.021). Pancreas‐specific complications and morbidity/mortality rates were similar. Conclusion: LAPD is a safe alternative to OPD in a select group of patients for an institution starting out with minimally invasive PD, and can be used to bridge the learning curve required for total laparoscopic PD. [ABSTRACT FROM AUTHOR]

Background: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention., Methods: We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction., Results: ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested., Conclusions: ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)