ArticleApplied ergonomics2021
Sensor-based indicators of performance changes between sessions during robotic surgery training.
Article in Applied ergonomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Technology-enhanced training in basic robotic surgical skills: a systematic review.Journal of robotic surgery · 2026Pooled it
- Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in real-world settings.Frontiers in neuroergonomics · 2025Pooled it
- Using neuroimaging to assess brain activity and areas associated with surgical skills: a systematic review.Surgical endoscopy · 2024Pooled it
- Measures of performance and proficiency in robotic assisted surgery: a systematic review.Journal of robotic surgery · 2024Pooled it
- Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review.The British journal of surgery · 2024Pooled it
- Status of the application of artificial intelligence (AI) in robotic surgical skills training in the modern robotic era and future horizons: a systematic review.Surgical endoscopy · 2026Review
- Artificial intelligence in robotic urologic surgery: a scoping review.World journal of urology · 2026Article
- Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review.Journal of medical Internet research · 2025Article
- Review
- A Review of Recent Advancements in Sensor-Integrated Medical Tools.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Utilizing Eye Tracking to Assess Medical Student Non-Technical Performance During Scenario-Based Simulation: Results of a Pilot Study.Global surgical education : journal of the Association for Surgical Education · 2023Article
- Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm.Computer methods and programs in biomedicine · 2023Article
- Physiological Metrics of Surgical Difficulty and Multi-Task Requirement during Robotic Surgery Skills.Sensors (Basel, Switzerland) · 2023Article
- Using Task-Evoked Pupillary Response to Predict Clinical Performance during a Simulation Training.Healthcare (Basel, Switzerland) · 2023Article
- Assessing Cognitive Workload and Performance in the Operating Room: Challenges in Applying NASA-TLX Among Experienced Surgeons.ANZ journal of surgeryObservational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Training of surgeons is essential for safe and effective use of robotic surgery, yet current assessment tools for learning progression are limited. The objective of this study was to measure changes in trainees' cognitive and behavioral states as they progressed in a robotic surgeon training curriculum at a medical institution. Seven surgical trainees in urology who had no formal robotic training experience participated in the simulation curriculum. They performed 12 robotic skills exercises with varying levels of difficulty repetitively in separate sessions. EEG (electroencephalogram) activity and eye movements were measured throughout to calculate three metrics: engagement index (indicator of task engagement), pupil diameter (indicator of mental workload) and gaze entropy (indicator of randomness in gaze pattern). Performance scores (completion of task goals) and mental workload ratings (NASA-Task Load Index) were collected after each exercise. Changes in performance scores between training sessions were calculated. Analysis of variance, repeated measures correlation, and machine learning classification were used to diagnose how cognitive and behavioral states associate with performance increases or decreases between sessions. The changes in performance were correlated with changes in engagement index (r
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.