Evidence map›Paper›PMID 34441417›Full record

ArticleDiagnostics (Basel, Switzerland)2021

A Novel Quantitative Method for Tooth Grinding Surface Assessment Using 3D Scanning.

Benedikt Sagl, Ferida Besirevic-Bulic, Martina Schmid-Schwap, Brenda Laky, Klara Janjić, Eva Piehslinger, Xiaohui Rausch-Fan

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Benedikt SaglCenter of Clinical Research, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-6739-4222
Ferida Besirevic-BulicDivision of Prosthodontics, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.
Martina Schmid-SchwapDivision of Prosthodontics, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.
Brenda LakyCenter of Clinical Research, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0003-1198-4132
Klara JanjićCenter of Clinical Research, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.ORCID 0000-0002-8057-3567
Eva PiehslingerDivision of Prosthodontics, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.
Xiaohui Rausch-FanCenter of Clinical Research, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sleep bruxism is an oral parafunction that involves involuntary tooth grinding and clenching. Splints with a colored layer that gets removed during tooth grinding are a common tool for the initial diagnosis of sleep bruxism. Currently, such splints are either assessed qualitatively or using 2D photographs, leading to a non-neglectable error due to the 3D nature of the dentition. In this study we propose a new and fast method for the quantitative assessment of tooth grinding surfaces using 3D scanning and mesh processing. We assessed our diagnostic method by producing 18 standardized splints with 8 grinding surfaces each, giving us a total of 144 surfaces. Moreover, each splint was scanned and analyzed five times. The accuracy and repeatability of our method was assessed by computing the intraclass correlation coefficient (ICC) as well reporting means and standard deviations of surface measurements for intra- and intersplint measurements. An ICC of 0.998 was computed as well as a maximum standard deviation of 0.63 mm

Indexed as

diagnostic bruxism splintdigital dentistrysleep bruxism

Identifiers

PMID34441417
PMCPMC8392501

What OpenQuestion holds

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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.