ArticleCureus2022
Creation of a Proof-of-Concept 3D-Printed Spinal Lateral Access Simulator.
Article in Cureus, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Minimally invasive lateral lumbar interbody fusion (LLIF) offers advantages over traditional approaches, providing indirect decompression of neural elements and deformity correction while avoiding many challenges and risks of anterior and posterior approaches. Mastering this technique requires a specialized team, advanced equipment, and sufficient case exposure. Current training is limited to the classic educational model, and alternative training methods such as cadaver labs can be inconvenient, inaccessible, expensive, and incompatible with intraoperative neuromonitoring (IONM) systems. Objective The aim of this study was to create a proof-of-concept, low-cost, fully synthetic lateral lumbar surgical simulator and to increase awareness of the lack of current training alternatives. Methods Standard engineering design and expert interviews of attending neurosurgeons, nurses, engineers, and medical device representatives (n=20) were utilized to determine key elements for the simulator, physical characteristics of the components, and translational strategy. Physical and radiographic testing was performed on multiple thermoplastics to determine appropriateness for inclusion in the simulator. For evaluation of the concept, a descriptive slide deck and questionnaire were sent to 15 U.S. and 15 international surgeons who perform LLIF. Results The lateral access training model (LATM) features the following three components: torso casing, spine module, and IONM feature. This model utilizes operable ABS (acrylonitrile butadiene styrene) 3D-printed lumbar vertebrae, verified for anatomical accuracy and compatibility with fluoroscopy. Additionally, a novel neuromonitoring simulation algorithm was developed to train junior residents on neurological complications. To further highlight the need for lateral training models, 30/30 polled surgeons felt that this simulator has value for the field, 29/30 noted that they would have used the LATM if they had access during training, and 30/30 responded that they would encourage trainees to practice on the LATM. Conclusion The LATM is a first step to provide reliable and inexpensive basic lateral lumbar spine training. While this model is lacking some anatomical features, our simulator offers novel training elements for lateral lumbar transpsoas approaches, which lay the foundation for future models to be built. The need for this training exists, and current gaps in the approach to learning these complex techniques need to be filled due to the inconvenience, cost, and impracticability of standard cadaveric models.
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.