ArticleJournal of thoracic disease2024
Emergent robotic surgery conversions: improving operating room team performance through high fidelity simulations.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence applications in surgical education and training: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Prevention and management of pulmonary vascular complications in anatomic lung resections: a narrative review.Journal of thoracic disease · 2026Review
- Comparison of thoracotomy conversion rates and causes between VATS and RATS for primary lung cancer: a retrospective cohort study.General thoracic and cardiovascular surgery · 2026Article
- When seconds matter - introducing a surgical salvage strategy by emergency robotic surgery: a review.Patient safety in surgery · 2026Review
- The preparedness of urologists to manage cardiopulmonary arrest during robot-assisted surgery.Journal of robotic surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Although robotic surgery has gained popularity, safety concerns remain due to potential delay in addressing intraoperative hemorrhages since the surgeon is not at the bedside. This study aimed to test whether a training program for emergency robotic undocking protocols improved the performance of thoracic operating room (OR) teams. Methods: An emergency undocking protocol and checklists were created for massive hemorrhage during robotic thoracic surgery. In phase I, two OR teams participated in Results: All teams successfully converted from robot-assisted to open, with the attending at bedside within five minutes from the decision to convert, regardless of phase or education type. Phase I (control) teams had an average of 2.55 critical misses per team while the average was 0.25 for phase II teams (P=0.08). There was no significant difference between phases in time required for the surgeon to be at the bedside (average 132.2 seconds, P=0.64). Conclusions: Targeted education can lead to improved team performance. This study shows that high-fidelity simulation and didactic sessions can both be used to effectively teach emergency undocking protocols.
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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.