Evidence map›Paper›PMID 31195791›Full record

ArticleClinical and experimental otorhinolaryngology2019

Development of a Novel Intraoperative Neuromonitoring System Using an Accelerometer Sensor in Thyroid Surgery: A Porcine Model Study.

Eui-Suk Sung, Jin-Choon Lee, Sung-Chan Shin, Hyun-Keun Kwon, Han-Seul Na, Da-Hee Park, Seong-Wook Choi, Jung-Hoon Ro, Byung-Joo Lee

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and experimental otorhinolaryngology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Eui-Suk SungDepartment of Otolaryngology-Head and Neck Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University College of Medicine, Yangsan, Korea.
Jin-Choon LeeDepartment of Otolaryngology-Head and Neck Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University College of Medicine, Yangsan, Korea.
Sung-Chan ShinDepartment of Otolaryngology-Head and Neck Surgery, Medical Research Institute, Pusan National University Hospital, Pusan National University College of Medicine, Busan, Korea.
Hyun-Keun KwonDepartment of Otolaryngology-Head and Neck Surgery, Medical Research Institute, Pusan National University Hospital, Pusan National University College of Medicine, Busan, Korea.
Han-Seul NaDepartment of Otolaryngology-Head and Neck Surgery, Medical Research Institute, Pusan National University Hospital, Pusan National University College of Medicine, Busan, Korea.
Da-Hee ParkDepartment of Otolaryngology-Head and Neck Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University College of Medicine, Yangsan, Korea.
Seong-Wook ChoiDepartment of Otolaryngology-Head and Neck Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University College of Medicine, Yangsan, Korea.
Jung-Hoon RoDepartment of Biomedical Engineering, Pusan National University Hospital, Busan, Korea.
Byung-Joo LeeDepartment of Otolaryngology-Head and Neck Surgery, Medical Research Institute, Pusan National University Hospital, Pusan National University College of Medicine, Busan, Korea.
Pusan National University Hospital · KRPusan National University Yangsan Hospital · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe sensitivity and positive predictive value of widely used intraoperative neuromonitoring (IONM) using electromyography (EMG) of the vocalis muscle in thyroid surgery are controversial. Thus, we developed a novel IONM system with an accelerometer sensor that uses the piezoelectric effect instead of EMG to detect laryngeal twitching. The objective of this study was to evaluate the feasibility and safety of this novel IONM system during thyroid surgery in a porcine model.

methodsWe developed an accelerometer sensor that uses the piezoelectric effect to measure laryngeal twitching in three dimensions. This novel accelerometer sensor was placed in the anterior neck skin (transcutaneous) or postcricoid area. Stimulus thresholds, amplitude, and latency of laryngeal twitching measured using the accelerometer sensor were compared to those measured through EMG of the vocalis muscle.

resultsThe amplitudes of the accelerometer sensor at the anterior neck and postcricoid area were significantly lower than those of EMG because of differences in the measurement method used to evaluate laryngeal movement. However, no significant differences in stimulus thresholds between the EMG endotracheal tube and transcutaneous or postcricoid accelerometer sensors were observed.

conclusionAccelerometer sensors located at the anterior neck or postcricoid area were able to identify laryngeal twitching. The stimulus intensity measured with these sensors was equivalent to that from conventional vocalis EMG. Our novel IONM system with an accelerometer sensor that checks changes in surface acceleration can be an alternative to EMG of the vocalis muscle for IONM in the future.

Indexed as

Accelerometer SensorElectromyographyNeuromonitoringRecurrent Laryngeal NerveThyroidectomy

Identifiers

PMID31195791
PMCPMC6787476
OpenAlexW2949426222

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.