Pointing to success: a discussion of the role of professional achievements in the selection of specialist surgical trainees

BACKGROUND: In Australia and New Zealand, competitive selection processes for surgical specialty training programs often use a standardized curriculum vitae (CV) to assess criteria such as professional achievements. This review aims to assess the predictive validity, standardization, and implicit biases of these selection methods, as well as their implications for trainees and the diversity of surgical cohorts. METHODS: The 2023 CV scoring criteria were collected for all available specialty surgical programs in Australia and New Zealand. In 2023, each of the 11 surgical craft programs published publicly available standardized CV scoring criteria. In this study, scored items that constitute 'professional achievements' were recorded and tabulated. Observational analysis of the collected data was then conducted. RESULTS: In 2023, each of the 11 specialty surgical craft programs published publicly available structured CVs, of which 10/11 allocated points for professional achievements. Designated points for professional achievements were classified as awards, scholarships, committee positions, and prior training courses: 4/11 programs offered points for scholarships/grants, 6/11 programs offered points for academic and/or non-academic prizes, and 8/11 programs offered points for professional development courses. Observational analysis of these findings suggests that professional achievements are desired in training program applicants. CONCLUSION: Variability in medical school opportunities and inherent heterogeneity reduce the CV's efficacy, unfairly disadvantaging some applicants. Observational analysis of hence highlights the need for future research to assess potential updates in CV parameters to enhance predictive validity, reduce bias, and promote diversity.

A Pilot Survey of Patient Perspectives on an Artificial Intelligence-Generated Presenter in a Patient Information Video about Face-Down Positioning after Vitreoretinal Surgery.

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.

To be or not to be on: aspirin and coronary artery bypass graft surgery.

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.

Metamorphopsia with SMART Syndrome: A Case Report.

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.

Prediction of vitreomacular traction syndrome outcomes with deep learning: A pilot study.

PURPOSE: To investigate the potential of an Optical Coherence Tomography (OCT) based Deep-Learning (DL) model in the prediction of Vitreomacular Traction (VMT) syndrome outcomes. DESIGN: A single-centre retrospective review. METHODS: Records of consecutive adult patients attending the Royal Adelaide Hospital vitreoretinal clinic with evidence of spontaneous VMT were reviewed from January 2019 until May 2022. All patients with evidence of causes of cystoid macular oedema or secondary causes of VMT were excluded. OCT scans and outcome data obtained from patient records was used to train, test and then validate the models. RESULTS: For the deep learning model, ninety-five patient files were identified from the OCT (SPECTRALIS system; Heidelberg Engineering, Heidelberg, Germany) records. 25% of the patients spontaneously improved, 48% remained stable and 27% had progression of their disease, approximately. The final longitudinal model was able to predict 'improved' or 'stable' disease with a positive predictive value of 0.72 and 0.79, respectively. The accuracy of the model was greater than 50%. CONCLUSIONS: Deep-learning models may be utilised in real-world settings to predict outcomes of VMT. This approach requires further investigation as it may improve patient outcomes by aiding ophthalmologists in cross-checking management decisions and reduce the need for unnecessary interventions or delays.

Current management options for the treatment of refractory postoperative cystoid macular edema: A systematic review.

Post-operative cystoid macular edema (PCME) is an important complication following intraocular surgery that often resolves spontaneously without treatment. In some cases, PCME may persist despite initial medical therapy, which can adversely impact visual outcomes. Various topical, intraocular and systemic treatments exist for the prevention and management of cystoid macular edema; however, there is no consensus on treatment of refractory cases in the postoperative setting. In accordance with the PRISMA guidelines, we systematically reviewed 68 articles describing management options and their outcomes for treatment-resistant cases of PCME. The most commonly reported treatments included steroid (39 studies) and biological-based (17 studies) therapies. We provide an overview of the treatment options for refractory PCME.

Subcutaneous sodium valproate in palliative care: A systematic review.

BACKGROUND: Seizures are an important palliative symptom, the management of which can be complicated by patients' capacity to swallow oral medications. In this setting, and the wish to avoid intravenous access, subcutaneous infusions may be employed. Options for antiseizure medications that can be provided subcutaneously may be limited. Subcutaneous sodium valproate may be an additional management strategy. AIM: To evaluate the published experience of subcutaneous valproate use in palliative care, namely with respect to effectiveness and tolerability. DESIGN: A systematic review was registered (PROSPERO CRD42023453427), conducted and reported according to PRISMA reporting guidelines. DATA SOURCES: The databases PubMed, EMBASE and Scopus were searched for publications until August 11, 2023. RESULTS: The searches returned 429 results, of which six fulfilled inclusion criteria. Case series were the most common study design, and most studies included <10 individuals who received subcutaneous sodium valproate. There were three studies that presented results on the utility of subcutaneous sodium valproate for seizure control, which described it to be an effective strategy. One study also described it as an effective treatment for neuropathic pain. The doses were often based on presumed 1:1 oral to subcutaneous conversion ratios. Only one study described a local site adverse reaction, which resolved with a change of administration site. CONCLUSIONS: There are limited data on the use of subcutaneous sodium valproate in palliative care. However, palliative symptoms for which subcutaneous sodium valproate have been used successfully are seizures and neuropathic pain. The available data have described few adverse effects, supporting its use with an appropriate degree of caution.

Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis.

OBJECTIVES: To evaluate the effectiveness of safeguards to prevent large language models (LLMs) from being misused to generate health disinformation, and to evaluate the transparency of artificial intelligence (AI) developers regarding their risk mitigation processes against observed vulnerabilities. DESIGN: Repeated cross sectional analysis. SETTING: Publicly accessible LLMs. METHODS: In a repeated cross sectional analysis, four LLMs (via chatbots/assistant interfaces) were evaluated: OpenAI's GPT-4 (via ChatGPT and Microsoft's Copilot), Google's PaLM 2 and newly released Gemini Pro (via Bard), Anthropic's Claude 2 (via Poe), and Meta's Llama 2 (via HuggingChat). In September 2023, these LLMs were prompted to generate health disinformation on two topics: sunscreen as a cause of skin cancer and the alkaline diet as a cancer cure. Jailbreaking techniques (ie, attempts to bypass safeguards) were evaluated if required. For LLMs with observed safeguarding vulnerabilities, the processes for reporting outputs of concern were audited. 12 weeks after initial investigations, the disinformation generation capabilities of the LLMs were re-evaluated to assess any subsequent improvements in safeguards. MAIN OUTCOME MEASURES: The main outcome measures were whether safeguards prevented the generation of health disinformation, and the transparency of risk mitigation processes against health disinformation. RESULTS: Claude 2 (via Poe) declined 130 prompts submitted across the two study timepoints requesting the generation of content claiming that sunscreen causes skin cancer or that the alkaline diet is a cure for cancer, even with jailbreaking attempts. GPT-4 (via Copilot) initially refused to generate health disinformation, even with jailbreaking attempts-although this was not the case at 12 weeks. In contrast, GPT-4 (via ChatGPT), PaLM 2/Gemini Pro (via Bard), and Llama 2 (via HuggingChat) consistently generated health disinformation blogs. In September 2023 evaluations, these LLMs facilitated the generation of 113 unique cancer disinformation blogs, totalling more than 40 000 words, without requiring jailbreaking attempts. The refusal rate across the evaluation timepoints for these LLMs was only 5% (7 of 150), and as prompted the LLM generated blogs incorporated attention grabbing titles, authentic looking (fake or fictional) references, fabricated testimonials from patients and clinicians, and they targeted diverse demographic groups. Although each LLM evaluated had mechanisms to report observed outputs of concern, the developers did not respond when observations of vulnerabilities were reported. CONCLUSIONS: This study found that although effective safeguards are feasible to prevent LLMs from being misused to generate health disinformation, they were inconsistently implemented. Furthermore, effective processes for reporting safeguard problems were lacking. Enhanced regulation, transparency, and routine auditing are required to help prevent LLMs from contributing to the mass generation of health disinformation.

Prognostic value of left ventricular systolic function before vascular surgery: a systematic review.

BACKGROUND: Vascular surgery carries a high risk of post-operative cardiac complications. Recent studies have shown an association between asymptomatic left ventricular systolic dysfunction and increased risk of major adverse cardiovascular events (MACE). This systematic review aims to evaluate the prognostic value of left ventricular function as determined by left ventricular ejection fraction (LVEF) measured by resting echocardiography before vascular surgery. METHODS: This review conformed to PRISMA and MOOSE guidelines. PubMed, OVID Medline and Cochrane databases were searched from inception to 27 October 2022. Eligible studies assessed vascular surgery patients, with multivariable-adjusted or propensity-matched observational studies measuring LVEF via resting echocardiography and providing risk estimates for outcomes. The primary outcomes measures were all-cause mortality and congestive heart failure at 30 days. Secondary outcome included the composite outcome MACE. RESULTS: Ten observational studies were included (4872 vascular surgery patients). Studies varied widely in degree of left ventricular systolic dysfunction, symptom status, and outcome reporting, precluding reliable meta-analysis. Available data demonstrated a trend towards increased incidence of all-cause mortality, congestive heart failure and MACE in patients with pre-operative LVEF <50%. Methodological quality of the included studies was found to be of moderate quality according to the Newcastle Ottawa Checklist. CONCLUSION: The evidence surrounding the prognostic value of LVEF measurement before vascular surgery is currently weak and inconclusive. Larger scale, prospective studies are required to further refine cardiac risk prediction before vascular surgery.

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.

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.

Achieving equity: patient demographics and outcomes after surgical and non-surgical procedures in South Australia, 2022.

BACKGROUND: Although modern Australian healthcare systems provide patient-centred care, the ability to predict and prevent suboptimal post-procedural outcomes based on patient demographics at admission may improve health equity. This study aimed to identify patient demographic characteristics that might predict disparities in mortality, readmission, and discharge outcomes after either an operative or non-operative procedural hospital admission. METHODS: This retrospective cohort study included all surgical and non-surgical procedural admissions at three of the four major metropolitan public hospitals in South Australia in 2022. Multivariable logistic regression, with backwards selection, evaluated association between patient demographic characteristics and outcomes up to 90 days post-procedurally. RESULTS: 40 882 admissions were included. Increased likelihood of all-cause, post-procedure mortality in-hospital, at 30 days, and 90 days, were significantly associated with increased age (P < 0.001), increased comorbidity burden (P < 0.001), an emergency admission (P < 0.001), and male sex (P = 0.046, P = 0.03, P < 0.001, respectively). Identification as ATSI (P < 0.001) and being born in Australia (P = 0.03, P = 0.001, respectively) were associated with an increased likelihood of 30-day hospital readmission and decreased likelihood of discharge directly home, as was increased comorbidity burden (P < 0.001) and emergency admission (P < 0.001). Being married (P < 0.001) and male sex (P = 0.003) were predictive of an increased likelihood of discharging directly home; in contrast to increased age (P < 0.001) which was predictive of decreased likelihood of this occurring. CONCLUSIONS: This study characterized several associations between patient demographic factors present on admission and outcomes after surgical and non-surgical procedures, that can be integrated within patient flow pathways through the Australian healthcare system to improve healthcare equity.