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Introduction: Machine learning has previously been applied to text analysis. There is limited data regarding the acceptability or accuracy of such applications in medical education. This project examined medical student opinion regarding computer-based marking and evaluated the accuracy of deep learning (DL), a subtype of machine learning, in the scoring of medical short answer questions (SAQs). Methods: Fourth- and fifth-year medical students undertook an anonymised online examination. Prior to the examination, students completed a survey gauging their opinion on computer-based marking. Questions were marked by humans, and then a DL analysis was conducted using convolutional neural networks. In the DL analysis, following preprocessing, data were split into a training dataset (on which models were developed using 10-fold cross-validation) and a test dataset (on which performance analysis was conducted). Results: One hundred and eighty-one students completed the examination (participation rate 59.0%). While students expressed concern regarding the accuracy of computer-based marking, the majority of students agreed that computer marking would be more objective than human marking (67.0%) and reported they would not object to computer-based marking (55.5%). Regarding automated marking of SAQs, for 1-mark questions, there were consistently high classification accuracies (mean accuracy 0.98). For more complex 2-mark and 3-mark SAQs, in which multiclass classification was required, accuracy was lower (mean 0.65 and 0.59, respectively). Conclusions: Medical students may be supportive of computer-based marking due to its objectivity. DL has the potential to provide accurate marking of written questions, however further research into DL marking of medical examinations is required. [ABSTRACT FROM AUTHOR]

Introduction: Simulation is commonly used by health and education institutions to facilitate interprofessional learning (IPL). The use of simulation in IPL is resource intensive. Evidence of what works, and with whom, is important to inform practice, policymaking and further research. The aim of this scoping review was to summarise the existing literature on IPL involving medical students, where simulation was the teaching modality. This review examined a variety of simulation-based interventions used to teach IPL to medical students and identified key features and outcomes. Methods: The databases PubMed, Medline, EMBASE and PsychINFO were searched using the terms related to medical student and simulation combined with interprofessional. Included articles involved medical students alongside a student or practitioner from at least one other health profession taking part in at least one simulation session. Data extraction was performed by two authors using a standardised form. Results: It emerged that simulations of medical emergencies were the most common format to deliver IPL interventions. Most studies evaluated the success of their IPL intervention using the Readiness for Interprofessional Learning Scale (RIPLS). Conclusion: All studies were successful in improving student attitudes towards IPL and interprofessional collaboration when these were measured outcomes. Formal team training prior to simulation is effective in improving teamwork skills. IPL interventions with participants from a greater mix of professions have more positive results. [ABSTRACT FROM AUTHOR]

Background: Dupuytren disease is a common fibroproliferative disease that affects the palmar fascia of the hands. Currently, there is limited consensus regarding the optimal therapy for this condition, with treatment decisions based largely on surgeon preference. Therefore, the aim of this study was to determine which treatments are the most effective for Dupuytren disease., Method: A systematic review and network meta-analyses were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Medline, EMBASE, and Web of Science were searched for randomized trials comparing treatments for Dupuytren disease in adults. Eligible treatments included open limited fasciectomy, collagenase injection, and percutaneous needle fasciotomy. Study selection, data extraction, and quality appraisal were performed in duplicate. The methodological quality was evaluated with the Cochrane risk-of-bias critical appraisal tool., Results: Eleven randomized clinical trials were included in this study. At short-term (1-12 weeks) and long-term (2-5 years) time points, fasciectomy improved contracture release more than collagenase and needle fasciotomy as inferred by a lower total passive extension deficit. However, there was no difference between the groups regarding the best possible outcome at any time point. Fasciectomy was also superior in terms of recurrence and patient satisfaction compared with collagenase and needle fasciotomy, but only at later time points. There was no difference in skin damage-related and nerve damage-related complications following fasciectomy compared with other modalities. Risk of bias was generally moderate., Conclusions: Fasciectomy provides superior long-term advantages in terms of patient outcomes when compared with collagenase and needle fasciotomy. Larger trials with better blinding of outcome assessors are needed in the future., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: G.M. was the authorizer of a health technology assessment of the collagenase product for South Australia; however, this does not represent a conflict of interest with regard to this publication. The authors have no other conflicts of interest to declare.

Introduction: This study investigated the public's preference to a recognisable and meaningful signage for Automated External Defibrillators (AEDs) in alignment with ISO 7010 standards, aiming to identify improvements for better public awareness and response during out-of-hospital cardiac arrests (OHCA)., Methods: A survey was administered via SurveyMonkey® and Heart of the Nation's social media. The survey evaluated recognition of ISO signage colors and AED symbols, and preferences for alternative AED signs. Baseline data including geographic location, industry employment, and first aid training were collected., Results: A total of 935 responses were received (Heart of the Nation's social media (n = 244) Survey Monkey's (paid, and independent of Heart of the Nation, n = 691). There were 511 from the US and Canada (54.65 %), 222 from the UK and Europe (23.76 %), 133 from the Asia Pacific (14.22 %), 6 from South America (0.64 %), 2 from the Middle East (0.21 %), and 61 from other territories (6.53 %). Among participants, 455 (48.66 %) were first aid trained. The healthcare sector was the most common employment (n = 155, 16.58 %). Only 187 (20 %) participants correctly identified the ISO AED sign. The preferred sign was a yellow sign with a red heart and blue font, chosen by 252 (27 %) participants., Conclusion: Current ISO 7010 AED signage is not widely recognised, and is only correctly interpreted by a small percentage of the public. The study suggests a need for more intuitive and visually distinct signage, such as the preferred yellow sign, to improve visibility and understanding, thereby enhancing AED accessibility and usage in OHCA., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: The objective of this study was to evaluate the impact of dysglycemia on perioperative outcomes, in patients with and without diabetes, and how prior glycemic control modifies these relationships., Methods: Consecutive surgical patients admitted to six South Australian tertiary hospitals between 2017 and 2023 were included. Blood glucose levels within 48 h pre- and post-operatively were assessed in an adjusted analyses against a priori selected covariates. Dysglycemia metrics were hyperglycemia (>10.0 mmol/L), hypoglycemia (<4.0 mmol/L), glycemic variability (standard deviation of mean blood glucose >1.7 mmol/L), and stress hyperglycemic ratio (SHR). The primary outcome was hospital mortality., Results: Of 52 145 patients, 7490 (14.4%) had recognized diabetes. Inpatient mortality was observed in 787 patients (1.5%), of which 150 (19.1%) had diabetes mellitus. Hyperglycemia was associated with increased mortality in patients with diabetes (odds ratio [OR] = 2.99, 95% CI: 1.63-5.67, p = 0.004) but not in non-diabetics, who instead had an increased odds of intensive care unit (ICU) admission if hyperglycemic (OR = 1.95, 95% CI: 1.40-2.72, p < 0.0001). Glycemic variability was associated with increased mortality in patients with diabetes (OR = 1.46, 95% CI: 1.05-2.01, p < 0.05) but not in non-diabetics. Preoperative glycemic control (HbA1c) attenuated both of these associations in a dose-dependent fashion. Hypoglycemia was associated with increased mortality in non-diabetics (OR = 2.14, 95% CI: 1.92-2.37, p < 0.001) but not in patients with diabetes., Conclusions,: In surgical patients with diabetes, prior exposure to hyperglycemia attenuates the impact of perioperative hyperglycemia and glycemic variability on inpatient mortality and ICU admission. In patients without diabetes mellitus, all absolute thresholds of dysglycemia are associated with ICU admission, unlike those with diabetes, suggesting the need to use more relative measures such as the SHR., (© 2024 The Author(s). Journal of Diabetes published by Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Aspirin's role in secondary prevention for patients with known coronary artery disease (CAD) is well established, validated by numerous landmark trials over the past several decades. However, its perioperative use in coronary artery bypass graft (CABG) surgery remains contentious due to the delicate balance between the risks of thrombosis and bleeding. While continuation of aspirin in patients undergoing CABG following acute coronary syndrome is widely supported due to the high risk of re-infarction, the evidence is less definitive for elective CABG procedures. The literature indicates a significant benefit of aspirin in reducing cardiovascular events in CAD patients, yet its impact on perioperative outcomes in CABG surgery is less clear. Some studies suggest increased bleeding risks without substantial improvement in cardiac outcomes. Specific to elective CABG, evidence is mixed, with some data indicating no significant difference in thrombotic or bleeding complications whether aspirin is continued or withheld preoperatively. Advancements in pharmacological therapies and perioperative care have evolved significantly since the initial aspirin trials, raising questions about the contemporary relevance of earlier findings. Individualized patient assessments and the development of risk stratification tools are needed to optimize perioperative aspirin use in CABG surgery. Further research is essential to establish clearer guidelines and improve patient outcomes. The objective of this review is to critically evaluate the existing evidence into the optimal management of perioperative aspirin in elective CABG patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2024 Gupta, Kovoor, Leslie, Litwin, Stretton, Zaka, Kovoor, Bacchi, Bennetts and Maddern.)

Introduction: SMART syndrome is a rare complication of brain radiotherapy. This is the first described presentation of SMART syndrome with metamorphopsia, which responded to aspirin, verapamil, and high-dose L-arginine therapy., Case Presentation: A 43-year-old man presented with 3 weeks of migraine headaches with metamorphopsia and complex visual hallucinations affecting the left lower quadrant of both visual fields. This occurred on a background of high-dose radiotherapy for right cerebellar astrocytoma 32 years ago. MRI brain demonstrated unilateral gyriform enhancement and FLAIR hyperintense cortical swelling in the right occipital lobe consistent with SMART syndrome., Conclusion: Unusual presentations of SMART syndrome exist and require consideration in all patients with focal neurological deficit post-brain radiotherapy. Validated diagnostic and treatment modalities for SMART syndrome are urgently required., Competing Interests: The authors declare that there is no conflict of interest., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Purpose: Podcasts are an increasingly popular medium for medical education in the field of cardiology. However, evidence suggests that the quality of the information presented can be variable. The aim of our study was to assess the quality of the most popular cardiology podcasts on existing podcast streaming services, using tools designed to grade online medical education., Results: We analyzed the five most recent episodes from 28 different popular cardiology podcasts as of 20th of September, 2022 using the validated rMETRIQ and JAMA scoring tools. The median podcast length was 20 min and most episodes were hosted by professors, subspecialty discussants or consultant physicians (87.14%). Although most episodes had only essential content (85%), only a small proportion of episodes provided detailed references (12.9%), explicitly identified conflicts of interest (30.7%), described a review process (13.6%), or provided a robust discussion of the podcast's content (13.6%). We observed no consistent relationship between episode length, seniority of host or seniority of guest speaker with rMETRIQ or JAMA scores., Conclusions: Cardiology podcasts are a valuable remote learning tool for clinicians. However, the reliability, relevance, and transparency of information provided on cardiology podcasts varies widely. Streamlined standards for evaluation are needed to improve podcast quality., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Kamalanathan, Hains, Bacchi, Martin, Zaka, Slattery, Kovoor, Gupta, Psaltis and Kovoor.)

Background: Accurate outcome predictions for patients who had ischaemic stroke with successful reperfusion after endovascular thrombectomy (EVT) may improve patient treatment and care. Our study developed prediction models for key clinical outcomes in patients with successful reperfusion following EVT in an Australian population., Methods: The study included all patients who had ischaemic stroke with occlusion in the proximal anterior cerebral circulation and successful reperfusion post-EVT over a 7-year period. Multivariable logistic regression and Cox regression models, incorporating bootstrap and multiple imputation techniques, were used to identify predictors and develop models for key clinical outcomes: 3-month poor functional status; 30-day, 1-year and 3-year mortality; survival time., Results: A total of 978 patients were included in the analyses. Predictors associated with one or more poor outcomes include: older age (ORs for every 5-year increase: 1.22-1.40), higher premorbid functional modified Rankin Scale (ORs: 1.31-1.75), higher baseline National Institutes of Health Stroke Scale (ORs: 1.05-1.07) score, higher blood glucose (ORs: 1.08-1.19), larger core volume (ORs for every 10 mL increase: 1.10-1.22), pre-EVT thrombolytic therapy (ORs: 0.44-0.56), history of heart failure (outcome: 30-day mortality, OR=1.87), interhospital transfer (ORs: 1.42 to 1.53), non-rural/regional stroke onset (outcome: functional dependency, OR=0.64), longer onset-to-groin puncture time (outcome: 3-year mortality, OR=1.08) and atherosclerosis-caused stroke (outcome: functional dependency, OR=1.68). The models using these predictors demonstrated moderate predictive abilities (area under the receiver operating characteristic curve range: 0.752-0.796)., Conclusion: Our models using real-world predictors assessed at hospital admission showed satisfactory performance in predicting poor functional outcomes and short-term and long-term mortality for patients with successful reperfusion following EVT. These can be used to inform EVT treatment provision and consent., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Crossword puzzles have been utilised as a means of health professions education (HPE) gamification. A systematic review conducted in accordance with PRISMA guidelines was performed to evaluate the educational impact and describe the characteristics of crosswords in HPE contexts. Twenty-nine studies fulfilled inclusion criteria. Crossword puzzles are an enjoyable learning activity and provide positive educational impact. The available evidence suggests crossword puzzles increase student knowledge on objective measures., Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-024-02085-x., Competing Interests: Conflict of InterestOn behalf of all authors, the corresponding author states that there is no conflict of interest., (© The Author(s) 2024.)

Backgrounds: Rapid response team or medical emergency team (MET) calls are typically activated by significant alterations of vital signs in inpatients. However, the clinical significance of a specific criterion, blood pressure elevations, is uncertain., Objectives: The aim of this study was to evaluate the likelihood ratios associated with MET-activating vital signs, particularly in-patient hypertension, for predicting in-hospital mortality among general medicine inpatients who met MET criteria at any point during admission in a South Australian metropolitan teaching hospital., Results: Among the 15,734 admissions over a two-year period, 4282 (27.2%) met any MET criteria, with a positive likelihood ratio of 3.05 (95% CI 2.93 to 3.18) for in-hospital mortality. Individual MET criteria were significantly associated with in-hospital mortality, with the highest positive likelihood ratio for respiratory rate ≤ 7 breaths per minute (9.83, 95% CI 6.90 to 13.62), barring systolic pressure ≥ 200 mmHg (LR + 1.26, 95% CI 0.86 to 1.69)., Conclusions: Our results show that meeting the MET criteria for hypertension, unlike other criteria, was not significant associated with in-hospital mortality. This observation warrants further research in other patient cohorts to determine whether blood pressure elevations should be routinely included in MET criteria., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

Background: This scoping review aimed to characterise near-death experiences in the setting of cardiac arrest, a phenomenon that is poorly understood and may have clinical consequences., Method: PubMed/MEDLINE was searched to 23 July 2023 for prospective studies describing near-death experiences in cardiac arrest. PRISMA-ScR guidelines were adhered to. Qualitative and quantitative data were synthesised. Meta-analysis was precluded due to data heterogeneity., Results: 60 records were identified, of which 11 studies involving interviews were included from various countries. Sample size ranged from 28-344, and proportion of female patients (when reported) was 0-50%, with mean age (when reported) ranging 54-64 years. Comorbidities and reasons for cardiac arrest were heterogeneously reported. Incidence of near-death experiences in the included studies varied from 6.3% to 39.3%; with variation between in-hospital (6.3-39.3%) versus out-of-hospital (18.9-21.2%) cardiac arrest. Individual variables regarding patient characteristics demonstrated statistically significant association with propensity for near-death experiences. Reported content of near-death experiences tended to reflect the language of the questionnaires used, rather than the true language used by individual study participants. Three studies conducted follow-up, and all suggested a positive life attitude change, however one found significantly higher 30-day all-cause mortality in patients with near-death experiences versus those without, in non-controlled analysis., Conclusions: From prospective studies that have investigated the phenomenon, near-death experiences may occur in as frequent as over one-third of patients with cardiac arrest. Lasting effects may follow these events, however these could also be confounded by clinical characteristics., (© 2024. The Author(s).)

Objective: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation., Methods: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria., Results: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93., Significance: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral., Plain Language Summary: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia., (© 2024 The Authors. Epilepsia Open published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Trimethoprim-sulfamethoxazole (TMP-SMX) is a broad spectrum antibiotic in use for more than 50 years. It has an important indication as first line agent in the prophylaxis of opportunistic infections, particularly Pneumocystis jirovecii pneumonia (PJP), in immunosuppressed patients. For those who have a history of allergy or severe intolerance to TMP-SMX, pentamidine, dapsone or atovaquone may be substituted; however there is evidence that TMP-SMX offers superior coverage for PJP, toxoplasmosis, and nocardiosis. Compared to pentamidine, it has the added benefit of cost-effectiveness and self-administration as opposed to required hospital attendance for administration. Many patients who report a history of allergy or adverse reaction to TMP-SMX (or "sulfur allergy") will be found not to be allergic; and even those who are allergic may be able to be desensitized. The evaluation and, where appropriate, removal of TMP-SMX allergy label enables the use of TMP-SMX for prophylaxis against opportunistic infections. This is a cost-effective intervention to optimize antimicrobial prescribing and reduce the risk of opportunistic infections in immunosuppressed patients., (© 2023 The Authors.)

Introduction: Video education is a commonly used patient education tool. However, the impact of integrating artificial intelligence (AI) into video education remains unexplored. This study aimed to examine the acceptability of an AI-generated presenter in a patient information video about face-down positioning after vitreoretinal surgery., Method: We prospectively enrolled participants who were planned for vitreoretinal surgery in which postoperative face-down positioning was recommended at the Royal Adelaide Hospital between December 2022 and September 2023. Participants were preoperatively provided with an educational video presented by an AI-generated presenter, incorporated into a surveyredcap. A pre- and post-video questionnaire was administered electronically., Results: There were 15 participants included in the study. In the pre-video questionnaire, most participants rated their awareness of special equipment for positioning as "not aware" (33%) and "slightly aware" (33%). The median pre-video six-item Spielberger State-Trait Anxiety Inventory Score was 12 (interquartile range 12-15). In the post-video questionnaire, most participants rated the video's quality as "excellent" (73%) and would recommend it to others (73%). The majority of participants strongly agreed that they understood the AI presenter (60%), felt at ease with the presenter (60%), and trusted the presenter (60%). Four participants (22%) disagreed with the statement: "I was aware the presenter was computer generated.", Conclusions: Video-based education may provide information that patients find useful, particularly for physical maneuvers such as face-down positioning. The use of an AI-generated presenter was well-received by the majority of patients. Further research regarding the use of AI to develop educational video content is warranted., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Background and Objectives: The potential of artificial intelligence in medical practice is increasingly being investigated. This study aimed to examine OpenAI's ChatGPT in answering medical multiple choice questions (MCQ) in an Australian context., Method: We provided MCQs from the Australian Medical Council's (AMC) medical licencing practice examination to ChatGPT. The chatbot's responses were graded using AMC's online portal. This experiment was repeated twice., Results: ChatGPT was moderately accurate in answering the questions, achieving a score of 29/50. It was able to generate answer explanations to most questions (45/50). The chatbot was moderately consistent, providing the same overall answer to 40 of the 50 questions between trial runs., Discussion: The moderate accuracy of ChatGPT demonstrates potential risks for both patients and physicians using this tool. Further research is required to create more accurate models and to critically appraise such models.

Background/objectives: Optical coherence tomography angiography (OCTA) has been found to identify changes in the retinal microvasculature of people with various cardiometabolic factors. Machine learning has previously been applied within ophthalmic imaging but has not yet been applied to these risk factors. The study aims to assess the feasibility of predicting the presence or absence of cardiovascular conditions and their associated risk factors using machine learning and OCTA., Methods: Cross-sectional study. Demographic and co-morbidity data was collected for each participant undergoing 3 × 3 mm, 6 × 6 mm and 8 × 8 mm OCTA scanning using the Carl Zeiss CIRRUS HD-OCT model 5000. The data was then pre-processed and randomly split into training and testing datasets (75%/25% split) before being applied to two models (Convolutional Neural Network and MoblieNetV2). Once developed on the training dataset, their performance was assessed on the unseen test dataset., Results: Two hundred forty-seven participants were included. Both models performed best in predicting the presence of hyperlipidaemia in 3 × 3 mm scans with an AUC of 0.74 and 0.81, and accuracy of 0.79 for CNN and MobileNetV2 respectively. Modest performance was achieved in the identification of diabetes mellitus, hypertension and congestive heart failure in 3 × 3 mm scans (all with AUC and accuracy >0.5). There was no significant recognition for 6 × 6 and 8 × 8 mm for any cardiometabolic risk factor., Conclusion: This study demonstrates the strength of ML to identify the presence cardiometabolic factors, in particular hyperlipidaemia, in high-resolution 3 × 3 mm OCTA scans. Early detection of risk factors prior to a clinically significant event, will assist in preventing adverse outcomes for people., (© 2023. The Author(s), under exclusive licence to The Royal College of Ophthalmologists.)

The epidemiology of elevations in blood pressure is incompletely characterized, particularly in Australia. Given the lack of evidence regarding the frequency and the optimal management of in-hospital hypertension, the authors performed a multicenter retrospective cohort study of consecutive medical admissions in South Australia over a 2-year period to investigate systolic blood pressure levels and their association with in-hospital mortality. Among 16 896 inpatients, 76% had at least one systolic blood pressure reading of ≥140 mmHg and 11.7% of ≥180 mmHg during hospitalization. A statistically significant negative relationship was observed between having at least one reading ≥140 mmHg and a likelihood of in-hospital mortality (odds ratio 0.41, 95% CI: 0.35 to 0.49, P < .001). Our results suggest that elevations in systolic blood pressure are common in Australian medical inpatients. However, the inverse association observed between systolic blood pressure values ≥140 mmHg and in-hospital mortality warrants further research to determine the clinical significance and optimal management of blood pressure elevations in this group., (© 2023 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC.)

Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection., (© The Author(s) 2023. Published by Oxford University Press on behalf of International Society for Quality in Health Care.)

Background: Depression is the leading cause of global disability and can develop following the change in body image and functional capacity associated with stoma surgery. However, reported prevalence across the literature is unknown. Accordingly, we performed a systematic review and meta-analysis aiming to characterise depressive symptoms after stoma surgery and potential predictive factors., Methods: PubMed/MEDLINE, Embase, CINAHL and Cochrane Library were searched from respective database inception to 6 March 2023 for studies reporting rates of depressive symptoms after stoma surgery. Risk of bias was assessed using the Downs and Black checklist for non-randomised studies of interventions (NRSIs), and Cochrane RoB2 tool for randomised controlled trials (RCTs). Meta-analysis incorporated meta-regressions and a random-effects model., Registration: PROSPERO, CRD42021262345., Results: From 5,742 records, 68 studies were included. According to Downs and Black checklist, the 65 NRSIs were of low to moderate methodological quality. According to Cochrane RoB2, the three RCTs ranged from low risk of bias to some concerns of bias. Thirty-eight studies reported rates of depressive symptoms after stoma surgery as a proportion of the respective study populations, and from these, the median rate across all timepoints was 42.9% 42.9% (IQR: 24.2-58.9%). Pooled scores for respective validated depression measures (Hospital Anxiety and Depression Score (HADS), Beck Depression Inventory (BDI), and Patient Health Questionnaire-9 (PHQ-9)) across studies reporting those scores were below clinical thresholds for major depressive disorder according to severity criteria of the respective scores. In the three studies that used the HADS to compare non-stoma versus stoma surgical populations, depressive symptoms were 58% less frequent in non-stoma populations. Region (Asia-Pacific; Europe; Middle East/Africa; North America) was significantly associated with postoperative depressive symptoms (p = 0.002), whereas age (p = 0.592) and sex (p = 0.069) were not., Conclusions: Depressive symptoms occur in almost half of stoma surgery patients, which is higher than the general population, and many inflammatory bowel disease and colorectal cancer populations outlined in the literature. However, validated measures suggest this is mostly at a level of clinical severity below major depressive disorder. Stoma patient outcomes and postoperative psychosocial adjustment may be enhanced by increased psychological evaluation and care in the perioperative period., (© 2023. The Author(s).)

Background: Strategies to improve the selection of appropriate target journals may reduce delays in disseminating research results. Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles., Objective: We sought to evaluate the performance of open-source artificial intelligence to predict the impact factor or Eigenfactor score tertile using academic article abstracts., Methods: PubMed-indexed articles published between 2016 and 2021 were identified with the Medical Subject Headings (MeSH) terms "ophthalmology," "radiology," and "neurology." Journals, titles, abstracts, author lists, and MeSH terms were collected. Journal impact factor and Eigenfactor scores were sourced from the 2020 Clarivate Journal Citation Report. The journals included in the study were allocated percentile ranks based on impact factor and Eigenfactor scores, compared with other journals that released publications in the same year. All abstracts were preprocessed, which included the removal of the abstract structure, and combined with titles, authors, and MeSH terms as a single input. The input data underwent preprocessing with the inbuilt ktrain Bidirectional Encoder Representations from Transformers (BERT) preprocessing library before analysis with BERT. Before use for logistic regression and XGBoost models, the input data underwent punctuation removal, negation detection, stemming, and conversion into a term frequency-inverse document frequency array. Following this preprocessing, data were randomly split into training and testing data sets with a 3:1 train:test ratio. Models were developed to predict whether a given article would be published in a first, second, or third tertile journal (0-33rd centile, 34th-66th centile, or 67th-100th centile), as ranked either by impact factor or Eigenfactor score. BERT, XGBoost, and logistic regression models were developed on the training data set before evaluation on the hold-out test data set. The primary outcome was overall classification accuracy for the best-performing model in the prediction of accepting journal impact factor tertile., Results: There were 10,813 articles from 382 unique journals. The median impact factor and Eigenfactor score were 2.117 (IQR 1.102-2.622) and 0.00247 (IQR 0.00105-0.03), respectively. The BERT model achieved the highest impact factor tertile classification accuracy of 75.0%, followed by an accuracy of 71.6% for XGBoost and 65.4% for logistic regression. Similarly, BERT achieved the highest Eigenfactor score tertile classification accuracy of 73.6%, followed by an accuracy of 71.8% for XGBoost and 65.3% for logistic regression., Conclusions: Open-source artificial intelligence can predict the impact factor and Eigenfactor score of accepting peer-reviewed journals. Further studies are required to examine the effect on publication success and the time-to-publication of such recommender systems., (©Carmelo Macri, Stephen Bacchi, Sheng Chieh Teoh, Wan Yin Lim, Lydia Lam, Sandy Patel, Mark Slee, Robert Casson, WengOnn Chan. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 07.03.2023.)

Introduction: Accurate assignment of procedural codes has important medico-legal, academic, and economic purposes for healthcare providers. Procedural coding requires accurate documentation and exhaustive manual labour to interpret complex operation notes. Ophthalmology operation notes are highly specialised making the process time-consuming and challenging to implement. This study aimed to develop natural language processing (NLP) models trained by medical professionals to assign procedural codes based on the surgical report. The automation and accuracy of these models can reduce burden on healthcare providers and generate reimbursements that reflect the operation performed., Methods: A retrospective analysis of ophthalmological operation notes from two metropolitan hospitals over a 12-month period was conducted. Procedural codes according to the Medicare Benefits Schedule (MBS) were applied. XGBoost, decision tree, Bidirectional Encoder Representations from Transformers (BERT) and logistic regression models were developed for classification experiments. Experiments involved both multi-label and binary classification, and the best performing model was used on the holdout test dataset., Results: There were 1,000 operation notes included in the study. Following manual review, the five most common procedures were cataract surgery (374 cases), vitrectomy (298 cases), laser therapy (149 cases), trabeculectomy (56 cases), and intravitreal injections (49 cases). Across the entire dataset, current coding was correct in 53.9% of cases. The BERT model had the highest classification accuracy (88.0%) in the multi-label classification on these five procedures. The total reimbursement achieved by the machine learning algorithm was $184,689.45 ($923.45 per case) compared with the gold standard of $214,527.50 ($1,072.64 per case)., Conclusion: Our study demonstrates accurate classification of ophthalmic operation notes into MBS coding categories with NLP technology. Combining human and machine-led approaches involves using NLP to screen operation notes to code procedures, with human review for further scrutiny. This technology can allow the assignment of correct MBS codes with greater accuracy. Further research and application in this area can facilitate accurate logging of unit activity, leading to reimbursements for healthcare providers. Increased accuracy of procedural coding can play an important role in training and education, study of disease epidemiology and improve research ways to optimise patient outcomes., (© 2023 The Author(s). Published by S. Karger AG, Basel.)

Purpose: The health care industry is an inherently resource-intense sector. Emerging technologies such as artificial intelligence (AI) are at the forefront of advancements in health care. The health economic implications of this technology have not been clearly established and represent a substantial barrier to adoption both in Australia and globally. This review aims to determine the health economic impact of implementing AI to ophthalmology in Australia., Methods: A systematic search of the databases PubMed/MEDLINE, EMBASE, and CENTRAL was conducted to March 2022, before data collection and risk of bias analysis in accordance with preferred reporting items for systematic ceviews and meta-analyses 2020 guidelines (PROSPERO number CRD42022325511). Included were full-text primary research articles analyzing a population of patients who have or are being evaluated for an ophthalmological diagnosis, using a health economic assessment system to assess the cost-effectiveness of AI., Results: Seven articles were identified for inclusion. Economic viability was defined as direct cost to the patient that is equal to or less than costs incurred with human clinician assessment. Despite the lack of Australia-specific data, foreign analyses overwhelmingly showed that AI is just as economically viable, if not more so, than traditional human screening programs while maintaining comparable clinical effectiveness. This evidence was largely in the setting of diabetic retinopathy screening., Conclusions: Primary Australian research is needed to accurately analyze the health economic implications of implementing AI on a large scale. Further research is also required to analyze the economic feasibility of adoption of AI technology in other areas of ophthalmology, such as glaucoma and cataract screening., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2022 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

Importance: Small study effects are the phenomena that studies with smaller sample sizes tend to report larger and more favorable effect estimates than studies with larger sample sizes., Objective: To evaluate the presence and extent of small study effects in diagnostic imaging accuracy meta-analyses., Data Sources: A search was conducted in the PubMed database for diagnostic imaging accuracy meta-analyses published between 2010 and 2019., Study Selection: Meta-analyses with 10 or more studies of medical imaging diagnostic accuracy, assessing a single imaging modality, and providing 2 × 2 contingency data were included. Studies that did not assess diagnostic accuracy of medical imaging techniques, compared 2 or more imaging modalities or different methods of 1 imaging modality, were cost analyses, used predictive or prognostic tests, did not provide individual patient data, or were network meta-analyses were excluded., Data Extraction and Synthesis: Data extraction was performed in accordance with the PRISMA guidelines., Main Outcomes and Measures: The diagnostic odds ratio (DOR) was calculated for each primary study using 2 × 2 contingency data. Regression analysis was used to examine the association between effect size estimate and precision across meta-analyses., Results: A total of 31 meta-analyses involving 668 primary studies and 80 206 patients were included. Fixed effects analysis produced a regression coefficient for the natural log of DOR against the SE of the natural log of DOR of 2.19 (95% CI, 1.49-2.90; P < .001), with computed tomography as the reference modality. Interaction test for modality and SE of the natural log of DOR did not depend on modality (Wald statistic P = .50). Taken together, this analysis found an inverse association between effect size estimate and precision that was independent of imaging modality. Of 26 meta-analyses that formally assessed for publication bias using funnel plots and statistical tests for funnel plot asymmetry, 21 found no evidence for such bias., Conclusions and Relevance: This meta-analysis found evidence of widespread prevalence of small study effects in the diagnostic imaging accuracy literature. One likely contributor to the observed effects is publication bias, which can undermine the results of many meta-analyses. Conventional methods for detecting funnel plot asymmetry conducted by included studies appeared to underestimate the presence of small study effects. Further studies are required to elucidate the various factors that contribute to small study effects.

Background: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an immunologic assessment. Machine learning natural language processing may be able to assist with the categorisation and risk stratification of penicillin ADRs., Objective: The aim of this study was to use text entered into an EHR to derive and evaluate machine learning models to classify penicillin ADRs and assess the risk of true allergy., Methods: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system EHR. The model was developed by training on labelled dataset. ADR entries were split into training and testing datasets and used to develop and test a variety of machine learning models. These were compared to categorisation with a simple algorithm using keyword search., Results: The best performing model for the classification of penicillin ADRs as being consistent with allergy or intolerance was the artificial neural network (AUC 0.994, sensitivity 0.99, specificity 0.96). The artificial neural network also achieved the highest AUC in the classification of high- or low-risk of true allergy (AUC 0.988, sensitivity 0.99, specificity 0.99). All ADR labels were able to be classified using these machine learning models, whereas a small proportion were unclassifiable using the simple algorithm as they contained no keywords., Conclusion: Machine learning natural language processing performed similarly to expert criteria in classifying and risk stratifying penicillin ADRs labels. These models outperformed simpler algorithms in their ability to interpret free-text data contained in the EHR. The automated evaluation of penicillin ADR labels may allow real-time risk stratification to facilitate delabelling and improve the specificity of prescribing alerts., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Growing evidence suggests that an altered microbiota composition contributes to the pathogenesis and clinical features in celiac disease (CD). We performed a comparative analysis of the gut microbiota in adulthood CD to evaluate whether: (i) dysbiosis anticipates mucosal lesions, (ii) gluten-free diet restores eubiosis, (iii) refractory CD has a peculiar microbial signature, and (iv) salivary and fecal communities overlap the mucosal one., Methods: This is a cross-sectional study where a total of 52 CD patients, including 13 active CD, 29 treated CD, 4 refractory CD, and 6 potential CD, were enrolled in a tertiary center together with 31 controls. A 16S rRNA-based amplicon metagenomics approach was applied to determine the microbiota structure and composition of salivary, duodenal mucosa, and stool samples, followed by appropriate bioinformatic analyses., Results: A reduction of both α- and β-diversity in CD, already evident in the potential form and achieving nadir in refractory CD, was evident. Taxonomically, mucosa displayed a significant abundance of Proteobacteria and an expansion of Neisseria , especially in active patients, while treated celiacs showed an intermediate profile between active disease and controls. The saliva community mirrored the mucosal one better than stool., Conclusion: Expansion of pathobiontic species anticipates villous atrophy and achieves the maximal divergence from controls in refractory CD. Gluten-free diet results in incomplete recovery. The overlapping results between mucosal and salivary samples indicate the use of saliva as a diagnostic fluid.

Background: It remains unclear whether brain fog is related to impaired cerebral blood flow (CBF) in postural tachycardia syndrome (POTS) patients., Methods: We assessed CBF in the posterior cerebral artery (PCA) using transcranial Doppler with visual stimuli in 11 POTS and 8 healthy subjects in the seated position, followed by neurocognitive testing., Results: CBF parameters were similar between the two groups. POTS patients demonstrated significantly longer latency in delayed match to sample response time and greater errors in attention switching task., Conclusions: Impaired short-term memory and alertness may underlie the symptom of brain fog in POTS patients, despite normal CBF., Competing Interests: The University of Adelaide reports having received on behalf of Dr Lau lecture and/or consulting fees from Abbott Medical, Bayer, Boehringer Ingelheim, Biotronik, BMS Pfizer, and Medtronic., (© 2020 The Authors. Journal of Arrhythmia published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Heart Rhythm Society.)

Rationale and Objectives: Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection., Materials and Methods: Clinical data regarding consecutive patients who received intravenous thrombolysis across two tertiary hospitals over a 7-year period were extracted from existing databases. The noncontrast computed tomography brain scans for these patients were then retrieved with hospital picture archiving and communication systems. Using a combination of convolutional neural networks (CNN) and artificial neural networks (ANN) several models were developed to predict either improvement in the National Institutes of Health Stroke Scale of ≥4 points at 24 hours ("NIHSS24"), or modified Rankin Scale 0-1 at 90 days ("mRS90"). The developed CNN and ANN were then applied to a test set. The THRIVE, HIAT, and SPAN-100 scores were also calculated for the patients in the test set and used to predict NIHSS24 and mRS90., Results: Data from 204 individuals were included in the project. The best performing DL model for prediction of mRS90 was a combination CNN + ANN based on clinical data and computed tomography brain (accuracy = 0.74, F1 score = 0.69). The best performing model for NIHSS24 prediction was also the combination CNN + ANN (accuracy = 0.71, F1 score = 0.74)., Conclusion: DL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated., (Copyright © 2019 The Association of University Radiologists. All rights reserved.)

The present study discusses about the effects of a combination of probiotics able to stimulate the immune system of patients affected by Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME). To this purpose, patients diagnosed according to Fukuda's criteria and treated with probiotics were analyzed by means of clinical and laboratory evaluations, before and after probiotic administrations. Probiotics were selected considering the possible pathogenic mechanisms of ME/CFS syndrome, which has been associated with an impaired immune response, dysregulation of Th1/Th2 ratio, and high oxidative stress with exhaustion of antioxidant reserve due to severe mitochondrial dysfunction. Immune and oxidative dysfunction could be related with the gastrointestinal (GI) chronic low-grade inflammation in the lamina propria and intestinal mucosal surface associated with dysbiosis, leaky gut, bacterial translocation, and immune and oxidative dysfunction. Literature data demonstrate that bacterial species are able to modulate the functions of the immune and oxidative systems and that the administration of some probiotics can improve mucosal barrier function, modulating the release of proinflammatory cytokines, in CFS/ME patients. This study represents a preliminary investigation to verifying the safety and efficacy of a certain combination of probiotics in CFS/ME patients. The results suggest that probiotics can modify the well-being status as well as inflammatory and oxidative indexes in CFS/ME patients. No adverse effects were observed except for one patient, which displayed a flare-up of symptoms, although all inflammatory parameters (i.e., cytokines, fecal calprotectin, ESR, and immunoglobulins) were reduced after probiotic intake. The reactivation of fatigue symptoms in this patient, whose clinical history reported the onset of CFS/ME following mononucleosis, could be related to an abnormal stimulation of the immune system as suggested by a recent study describing an exaggerated immune activation associated with chronic fatigue., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2019 Letizia Venturini et al.)