Publications & Research Outputs

Introduction With more robotic platforms approved for clinical use, interest in assessing skill transferability has increased. Skill transfer has yet to be studied amongst trainees in early stages of robotic training. This systematic review evaluated skill transferability of robotic platforms amongst trainees in simulated settings. Methods The systematic review was conducted according to PRISMA guidelines. Five databases were searched from inception until 20 January 2025. Inclusion criteria had studies using simulation-based modalities, more than one robotic platform, and objective metrics or global rating scales for skills assessment. Results Search identified 609 unique studies. Five studies were included. Platforms analysed were Da Vinci X, Si and single-port system, Hugo RAS and CMR Versius. No performance differences seen across platforms amongst novices and experts. Groups with some robotic experience exhibited variable degrees of skill transfer. Prior robotic surgical experience was the main driver for better baseline performance and skills progression. Conclusion Skill transferability was demonstrated in cross-platform simulated training amongst experts and novices, indicating that core robotic surgical skills can be applied across systems. Less evidence of skill transfer in intermediate-level participants suggests that those still consolidating their technical proficiency may be more sensitive to platform-specific differences. Console design variations appeared to influence transferability. Incorporating cross-platform training could enhance trainees’ understanding of the fundamental principles of robotic surgery, equipping them with more adaptable skillsets suited to various platforms. This has important implications for surgical training, as during the trainees’ learning phase, they should be aware of the potential decline in technical performance when transitioning between platforms.

Background Burnout is increasingly identified as a factor affecting physician welfare, and subsequently patient care. Surgeons are known to experience higher rates of burnout compared with the general population. No review has focused exclusively on burnout prevalence among paediatric surgeons. This systematic review and meta-analysis aims to determine the prevalence of burnout among paediatric surgeons, alongside identifying and analysing potential influences. Methods Using Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology guidelines, following our published PROSPERO protocol (CRD42025640570), 719 records were identified from inception through 13 January 2025. Included studies produced primary data reporting paediatric surgeon burnout prevalence, using validated burnout metrics. Main exclusion criteria were: study or population irrelevance, study type and duplicates. All available data were extracted; authors were contacted for original data. Random-effects meta-analysis with restricted maximum likelihood estimation was conducted to estimate a pooled burnout prevalence. Further subgroup analyses and meta-regressions were performed. Results Meta-analysis of 15 studies (2757 paediatric surgeons) revealed a pooled burnout prevalence of 29.4% (95% CI 20.3% to 40.5%), with substantial heterogeneity (I²=96.4%). Burnout rates ranged from 7.8% to 73.7%. Studies using the Maslach Burnout Inventory reported significantly lower burnout (22.4%) compared with non-MBI tools (44.9%; p=0.025). No publication bias was detected. Study quality and measurement tool choice explained 35.3% and 24.5% of heterogeneity, respectively. Conclusions Nearly one in three paediatric surgeons experience burnout. Although concerning, this prevalence is lower than in other surgical disciplines. Strong personal accomplishment from emotionally rewarding work with children appears protective, alongside structurally embedded practices such as family-centred care and multidisciplinary collaboration. Personality traits associated with agreeableness, conscientiousness and emotional stability may further reduce susceptibility. Specialty-specific interventions supported by structured mentorship are critical to sustainable well-being in paediatric surgical practice.

This study investigates whether objective hand-tracking kinematic metrics can serve as reliable correlates of expert reviewer-assessed robotic surgical skill. Using a longitudinal pilot design, the work explores the potential role of motion-based performance analytics in objective assessment, feedback, and progression modelling in robotic surgical training.

This work describes the acquisition and structure of a multimodal dataset capturing kinematic and complementary data streams across two robotic surgical platforms. The dataset supports objective skill evaluation, performance analytics, simulation-based training research, and future machine-learning approaches to robotic surgical assessment.

This oral presentation, delivered by Callum Pearse, outlined a multi-modal, system-agnostic pipeline for objective robotic surgical training assessment. The approach focuses on motion-derived kinematic metrics and complementary data streams to support objective evaluation, feedback, and progression monitoring across different robotic platforms.

This study investigates whether objective hand-tracking kinematic metrics can serve as reliable correlates of expert reviewer-assessed robotic surgical skill. Using a longitudinal pilot design, the work explores the potential role of motion-based performance analytics in objective assessment, feedback, and progression modelling in robotic surgical training.

This work compares early skill acquisition and learning-curve progression amongst novice trainees using the Hugo RAS and Da Vinci X robotic platforms. The study contributes to understanding whether platform-related differences affect early technical development and the design of cross-platform robotic surgical training. Callum Pearse will present the poster at ERUS26 in Padua.

This poster presented the acquisition and structure of a multimodal dataset capturing kinematic and complementary data streams across two robotic surgical platforms. The work supports objective skill evaluation, simulation-based training research, and future machine-learning approaches to robotic surgical performance assessment.

Background The increasing use of surgical robotic systems has driven higher training demand, highlighting the need for objective metrics to standardise training, improve learning curves, and optimise patient outcomes. As a pilot exploration, bimanual dexterity may offer a robust, task-independent, objective metric that distinguishes expertise. Objectives To analyse the correlation between left- and right-hand workspace volumes in novice and intermediate robotic surgeons to determine whether this relationship reflects bimanual dexterity and can be utilised as an objective performance measure of expertise. Methods This pilot study collected data from 21 participants, with 4 excluded due to incomplete datasets. Depth cameras captured kinematic data from 11 novices and 6 intermediates. Spearman’s correlation assessed workspace volume relationships, with 95% confidence intervals derived via bootstrapping. Results Novices demonstrated a moderate negative correlation (Spearman’s ρ=-0.4636, p=0.1509, 95% CI: -0.9720-0.3116) whereas intermediates showed a weak positive correlation (ρ=0.2571, p=0.6228, 95% CI: -1.0000-1.0000). Despite no statistical significance, intermediates exhibited more synchronised workspace utilisation, potentially indicating greater bimanual dexterity. Patient Benefit Objective metrics of expertise could enhance training, improve patient outcomes by ensuring proficiency before independent practice, and enable standardisation of robotic surgical training. Conclusion As a pilot study with a small sample size, this provides preliminary insight into a possible correlation between left- and right-hand workspaces in novices and intermediates. Ongoing data collection and analysis may prove a statistically significant relationship, allowing the integration of this metric into a machine learning model to provide feedback to trainees and enable predictions of the effectiveness of surgical skill acquisition.

This conference paper reports exploratory analysis of hand-specific workspace characteristics during robotic skills training. By comparing left- and right-hand workspaces, the study contributes to understanding bimanual coordination and task allocation in robotic skill acquisition. Findings support the potential role of hand-specific metrics in informing training design and performance assessment.