ArticleJournal of neurosurgery2019
Improving patient safety during introduction of novel medical devices through cumulative summation analysis.
Article in Journal of neurosurgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Comparison of robotic and manual implantation of intracerebral electrodes: a single-centre, single-blinded, randomised controlled trial.Scientific reports · 2021Trial
- Safety of Simultaneous Scalp and Intracranial EEG and fMRI: Evaluation of RF-Induced Heating.Bioengineering (Basel, Switzerland) · 2025Article
- The learning curve for minimally invasive Achilles repair using the "lumbar puncture needle and oval forceps" technique.BMC musculoskeletal disorders · 2024Article
- Learning curve of tibial cortex transverse transport: a cumulative sum analysis.Journal of orthopaedic surgery and research · 2023Article
- Article
- Primary Experiences with Robot-assisted Navigation-based Frameless Stereo-electroencephalography: Higher Accuracy than Neuronavigation-guided Manual Adjustment.Neurologia medico-chirurgica · 2022Article
- A novel technique for fence-post tube placement in glioma using the robot-guided frameless neuronavigation technique under exoscope surgery: patient series.Journal of neurosurgery. Case lessons · 2021Article
- The IDEAL framework in neurosurgery: a bibliometric analysis.Acta neurochirurgica · 2020Review
- Fuzzy Logic-Based Risk Assessment of a Parallel Robot for Elbow and Wrist Rehabilitation.International journal of environmental research and public health · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
OBJECTIVE The aim of this study was to implement cumulative summation (CUSUM) analysis as an early-warning detection and quality assurance system for preclinical testing of the iSYS1 novel robotic trajectory guidance system. METHODS Anatomically accurate 3D-printed skull phantoms were created for 3 patients who underwent implantation of 21 stereoelectroencephalography electrodes by surgeons using the current standard of care (frameless technique). Implantation schema were recreated using the iSYS1 system, and paired accuracy measures were compared with the previous frameless implantations. Entry point, target point, and implantation angle accuracy were measured on postimplantation CT scans. CUSUM analysis was undertaken prospectively. RESULTS The iSYS1 trajectory guidance system significantly improved electrode entry point accuracies from 1.90 ± 0.96 mm (mean ± SD) to 0.76 ± 0.57 mm (mean ± SD) without increasing implantation risk. CUSUM analysis was successful as a continuous measure of surgical performance and acted as an early-warning detection system. The surgical learning curve, although minimal, showed improvement after insertion of the eighth electrode. CONCLUSIONS The iSYS1 trajectory guidance system did not show any increased risk during phantom preclinical testing when used by neurosurgeons who had no experience with its use. CUSUM analysis is a simple technique that can be applied to all stages of the IDEAL (idea, development, exploration, assessment) framework as an extra patient safety mechanism. Further clinical trials are required to prove the efficacy of the device.
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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.