Impact of Axillary Lymph Node Dissection and Sentinel Lymph Node Biopsy on Upper Limb Morbidity in Breast Cancer Patients: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the impact of axillary lymph node dissection (ALND) and sentinel lymph node biopsy (SLNB) on upper limb (UL) morbidity in breast cancer patients. BACKGROUND: Axillary de-escalation is motivated by a desire to reduce harm of ALND. Understanding the impact of axillary surgery and disparities in operative procedures on postoperative arm morbidity would better direct resources to the point of need and cement the need for de-escalation strategies. METHODS: Embase, MEDLINE, CINAHL, and PsychINFO were searched from 1990 until March 2020. Included studies were randomized-controlled and observational studies focusing on UL morbidities, in breast surgery patients. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The prevalence of UL morbidity comparing SLNB and ALND at <12 months, 12 to 24 months, and beyond 24 months were analyzed. RESULTS: Sixty-seven studies were included. All studies reported a higher rate of lymphedema and pain after ALND compared with SLNB. The difference in lymphedema and pain prevalence between SLNB and ALND was 13.7% (95% confidence interval: 10.5-16.8, P <0.005) and 24.2% (95% confidence interval: 12.1-36.3, P <0.005), respectively. Pooled estimates for prevalence of reduced strength and range of motion after SLNB and ALND were 15.2% versus 30.9% and 17.1% versus 29.8%, respectively. Type of axillary surgery, greater body mass index, and radiotherapy were some of the predictors for UL morbidities. CONCLUSIONS: Prevalence of lymphedema after ALND was higher than previously estimated. ALND patients experienced greater rates of lymphedema, pain, reduced strength, and range of motion compared with SLNB. The findings support the continued drive to de-escalate axillary surgery.

Visualisation Approaches in Technology-Enhanced Medical Simulation Learning: Current Evidence and Future Directions.

Technology-enhanced learning (TEL) has been proposed as an approach to minimise the healthcare workforce shortage preventing universal healthcare coverage. Simulation-based medical education is a well-established teaching method. Little is known about effective strategies to translate in-person medical simulation teaching into a virtual world. This work aimed to review the literature on approaches to visualisation in technology-enhanced medical simulation. A systematic search strategy was optimised using three databases: Embase, MEDLINE, and APA PsycInfo. Additional papers were identified through cross-referencing. The last date of this search was 3 January 2022. The articles were analysed qualitatively. The risk of bias was assessed using ROBINS-I and RoB 2 tools. The search yielded 656 results with 9 additional papers identified through cross-referencing. Following deduplication and exclusions, 23 articles were included in a qualitative synthesis of evidence. Offline and online computer-based modules with virtual patient cases or practical skills simulations were identified as the most prevalent clinical simulation teaching modalities. Visualisation approaches included text, images, animations, videos, and 3D environments. Significant heterogeneity of study designs with a moderate risk of bias was established. Based on the current data, the virtual patient scenarios should use natural language input interfaces enriched with video and voice recordings, 3D animations, and short text descriptions to make the patient management experience more lifelike and increase knowledge retention. However, there is no agreed framework for assessing the pedagogical value of these innovations. High-quality randomised controlled trials of TEL-based clinical simulation are essential to advance the field.

Artificial intelligence in the detection, characterisation and prediction of hepatocellular carcinoma: a narrative review.

Hepatocellular carcinoma (HCC) is a significant cause of morbidity and mortality worldwide. Despite significant advancements in detection and treatment of HCC, its management remains a challenge. Artificial intelligence (AI) has played a role in medicine for several decades, however, clinically applicable AI-driven solutions have only started to emerge, due to gradual improvement in sensitivity and specificity of AI, and implementation of convoluted neural networks. A review of the existing literature has been conducted to determine the role of AI in HCC, and three main domains were identified in the search: detection, characterisation and prediction. Implementation of AI models into detection of HCC has immense potential, as AI excels at analysis and integration of large datasets. The use of biomarkers, with the rise of '-omics', can revolutionise the detection of HCC. Tumour characterisation (differentiation between benign masses, HCC, and other malignant tumours, as well as staging and grading) using AI was shown to be superior to classical statistical methods, based on radiological and pathological images. Finally, AI solutions for predicting treatment outcomes and survival emerged in recent years with the potential to shape future HCC guidelines. These AI algorithms based on a combination of clinical data and imaging-extracted features can also support clinical decision making, especially treatment choice. However, AI research on HCC has several limitations, hindering its clinical adoption; small sample size, single-centre data collection, lack of collaboration and transparency, lack of external validation, and model overfitting all results in low generalisability of the results that currently exist. AI has potential to revolutionise detection, characterisation and prediction of HCC, however, for AI solutions to reach widespread clinical adoption, interdisciplinary collaboration is needed, to foster an environment in which AI solutions can be further improved, validated and included in treatment algorithms. In conclusion, AI has a multifaceted role in HCC across all aspects of the disease and its importance can increase in the near future, as more sophisticated technologies emerge.

A visual analog scale for the assessment of mild sleepiness in patients with obstructive sleep apnea and healthy participants.

MOTIVATION: Studies have shown poor clinical effectiveness of the Epworth Sleepiness Scale (ESS) due to its ambiguity of items and cultural applicability. This study aimed to investigate the efficacy of a Visual Analog Scale (VAS) to assess sleepiness, compared to ESS. METHODS: Thirty-two obstructive sleep apnea (OSA) patients and 32 healthy participants completed two visits, 1 month apart, during which they completed both ESS and VAS. Patients diagnosed with OSA were treated with Continuous positive airway pressure (CPAP) between visits. The agreement between the ESS and VAS scores in both patients with OSA and healthy participants was investigated using Pearson correlation and Area Under the receiver operating characteristics. RESULTS: The (mean ± standard deviation) Oxygen Desaturation Index for patients with OSA was 18.5 ± 5.7 events/hour and 1.7 ± 1.0 events/hour in the healthy participants. A reduction in sleepiness, following CPAP treatment occurred in patients with OSA, using the ESS (11.2 ± 5.5-4.7 ± 5.0 points, P < 0.001) and the VAS (50.2 ± 3.0-21.9 ± 26.5 mm, P < 0.001). There was no significant change in sleepiness, in healthy participants using the ESS (3.91 ± 3.14-3.34 ± 3.27 points (P < 0.48) or the VAS (15.58 ± 21.21-12.05 ± 14.75 mm, (P < 0.44). A Likert scale showed that the VAS was easier to use compared to ESS in visit 1 (VAS: 8.7 ± 1.9 points, ESS: 7.7 ± 2.6 points, (P < 0.001), and visit 2 (VAS: 9.5 ± 1.4 points, ESS: 8.6 ± 1.5 points, P < 0.001). CONCLUSION: These preliminary results suggest that the VAS can detect a change in sleepiness after CPAP treatment in patients with OSA and that the VAS was also easier to use compared to ESS.

Early Years Research Elective: Changing Perspectives and Dealing with Uncertainty.

Uncertainty is ever-present within the medical profession. To effectively manage uncertainty, future doctors must develop key competencies including resilience, creativity and adaptability along with the capacity for collaboration and embracing multiculturalism. The authors believe that attending an overseas clinical research placement as a junior medical student can benefit medical education by offering an early opportunity to develop these qualities. These views are supported by reflections on a clinical research module and placement in Guangzhou, China completed by the authors during the second year of medical school and aims to highlight key learning opportunities.

Systematic review of combined functional near-infrared spectroscopy and transcranial direct-current stimulation studies.

Significance: Combining transcranial direct-current stimulation (tDCS) with functional near-infrared spectroscopy (fNIRS) is a recent approach to exploring brain activation evoked by neurostimulation. Aim: To critically evaluate studies combining tDCS and fNIRS and provide a consolidated overview of cortical hemodynamic responses to neurostimulation. Approach: Key terms were searched in three databases (MEDLINE, EMBASE, and PsycINFO) with cross-referencing and works from Google Scholar also evaluated. All studies reporting on fNIRS-derived hemoglobin changes evoked by tDCS were included. Results: Literature searches revealed 474 articles, of which 28 were included for final review (22 in healthy individuals: 9 involving rest and 13 with tasks; 6 in the clinical setting). At rest, an overall increase in cortical activation was observed in fNIRS responses at the site of stimulation, with evidence suggesting nonstimulated brain regions are also similarly affected. Conversely, during tasks, reduced cortical activation was observed during online stimulation. Offline and poststimulation effects were less consistent, as is the impact on clinical populations and their symptom correlation. Conclusion: This review explores the methodological frameworks for fNIRS-tDCS evaluations and summarizes hemodynamic responses associated with tDCS in all populations. Our findings provide further evidence of the impact of tDCS on neuronal activation within functionally connected networks.