ReviewDer Unfallchirurg2018
[Team training and assessment in mixed reality-based simulated operating room : Current state of research in the field of simulation in spine surgery exemplified by the ATMEOS project].
Review in Der Unfallchirurg, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 14 citations in OpenAlex.
- Reconstruction of the Sternoclavicular Joint With an Autologous Fibular Head: A Case Report.Clinical case reports · 2025Article
- Emerging Technologies within Spine Surgery.Life (Basel, Switzerland) · 2023Review
- Augmented Reality Integration in Manikin-Based Simulations: Bringing Basic Science to the Critical Care Bedside with Limited Augmented Reality Resources.Medical science educator · 2023Article
- Article
- Current innovation in virtual and augmented reality in spine surgery.Annals of translational medicine · 2021Review
- Application and Prospect of Mixed Reality Technology in Medical Field.Current medical science · 2019Review
- [Digitalization and artificial intelligence in orthopedics and traumatology].Der Orthopade · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Surgical simulators are being increasingly used as an attractive alternative to clinical training in addition to conventional animal models and human specimens. Typically, surgical simulation technology is designed for the purpose of teaching technical surgical skills (so-called task trainers). Simulator training in surgery is therefore in general limited to the individual training of the surgeon and disregards the participation of the rest of the surgical team. The objective of the project Assessment and Training of Medical Experts based on Objective Standards (ATMEOS) is to develop an immersive simulated operating room environment that enables the training and assessment of multidisciplinary surgical teams under various conditions. Using a mixed reality approach, a synthetic patient model, real surgical instruments and radiation-free virtual X‑ray imaging are combined into a simulation of spinal surgery. In previous research studies, the concept was evaluated in terms of realism, plausibility and immersiveness. In the current research, assessment measurements for technical and non-technical skills are developed and evaluated. The aim is to observe multidisciplinary surgical teams in the simulated operating room during minimally invasive spinal surgery and objectively assess the performance of the individual team members and the entire team. Moreover, the effectiveness of training methods and surgical techniques or success critical factors, e. g. management of crisis situations, can be captured and objectively assessed in the controlled environment.
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.