Evidence map›Paper›PMID 36379999›Full record

ArticleScientific reports2022

Preliminary study of the accuracy and safety of robot-assisted mandibular distraction osteogenesis with electromagnetic navigation in hemifacial microsomia using rabbit models.

Ziwei Zhang, Byeong Seop Kim, Wenqing Han, Mengzhe Sun, Xiaojun Chen, Yingjie Yan, Haisong Xu, Gang Chai, Li Lin

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 16% 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

6 citing papers in PubMed, 8 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.

Ziwei Zhang *Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Byeong Seop Kim *Department of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Wenqing HanDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Mengzhe SunDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Xiaojun ChenDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Yingjie YanDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Haisong XuDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China.
Gang ChaiDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China. chaig1081@sh9hospital.org.cn.
Li LinDepartment of Plastic and Reconstructive Surgery, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, 639 Zhi Zao Ju Road, Shanghai, 200011, China. linli991@sjtu.edu.cn.
Shanghai Ninth People's Hospital · CNShanghai Jiao Tong University · CN

Funding

Shanghai Municipal Key Clinical Specialty shslczdzk00901the Clinical Research Plan of SHDC SHDC2020CR3070Bthe Clinical Research Program of the 9th People's Hospital affiliated to Shanghai Jiao Tong university School of Medicine JYLJ202108the Shanghai Jiao Tong University School of Medicine Two-hundred Talent project 20161420
6 · The paper itself

Abstract

This study aimed to investigate the accuracy and safety of mandibular osteotomy and distraction device positioning in distraction osteogenesis assisted by an electromagnetic navigation surgical robot. Twelve New Zealand white rabbits were randomly divided into two groups after computed tomography. The control group underwent a procedure based on the preoperative three-dimensional design and clinical experience. Animals in experimental group underwent a procedure with robotic assistance after registration. The accuracies of osteotomy and distraction device positioning were analysed based on distance and angular errors. The change in ramus length after a 1 cm-extension of the distraction device was for assessing distraction effect. The preparation, operative and osteotomy times, intraoperative bleeding, and teeth injury were used for safety assessment. In the experimental group, the distance (t = 2.591, p = 0.011) and angular (t = 4.276, p = 0.002) errors of osteotomy plane, and the errors in distraction device position (t = 3.222, p = 0.009) and direction (t = 4.697, p = 0.001) were lower; the distraction effect was better (t = 4.096, p = 0.002). There was no significant difference in the osteotomy time and bleeding; however, the overall operative and preparation times were increased in the experimental group, with a reduced rate of teeth damage. Robot-assisted mandibular distraction osteogenesis with electromagnetic navigation in craniofacial microsomia is feasible, safe, significantly improves surgical precision.

Indexed as

Goldenhar SyndromeOsteogenesis, DistractionRoboticsSurgery, Computer-AssistedAnimalsElectromagnetic PhenomenaImaging, Three-DimensionalMandibleRabbits

Identifiers

PMID36379999
PMCPMC9666469
OpenAlexW4309192288

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.