Evidence map›Paper›PMID 37481612›Full record

ArticleScientific reports2023

Feasibility of software-based assessment for automated evaluation of tooth preparation for dental crown by using a computational geometric algorithm.

Sangjun Han, Yuseung Yi, Marta Revilla-León, Burak Yilmaz, Hyung-In Yoon

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Sangjun Han *School of Dentistry, Seoul National University, Seoul, Republic of Korea.
Yuseung Yi *Department of Prosthodontics, Seoul National University Dental Hospital, Seoul, Republic of Korea.
Marta Revilla-LeónDepartment of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, WA, USA.
Burak YilmazDepartment of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland.
Hyung-In YoonDepartment of Reconstructive Dentistry and Gerodontology, School of Dental Medicine, University of Bern, Bern, Switzerland. drhiy226@snu.ac.kr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The purpose of this study was to propose the concept of software-based automated evaluation (SAE) of tooth preparation quality using computational geometric algorithms, and evaluate the feasibility of SAE in the assessment of abutment tooth preparation for single-unit anatomic contour crowns by comparing it with a human-based digitally assisted evaluation (DAE) by trained human evaluators. Thirty-five mandibular first molars were prepared for anatomical contour crown restoration by graduate students. Each prepared tooth was digitized and evaluated in terms of occlusal reduction and total occlusal convergence using SAE and DAE. Intra-rater agreement for the scores graded by the SAE and DAE and inter-rater agreement between the SAE and DAE were analyzed with the significance level (α) of 0.05. The evaluation using the SAE protocol demonstrated perfect intra-rater agreement, whereas the evaluation using the DAE protocol showed moderate-to-good intra-rater agreement. The evaluation values of the SAE and DAE protocols showed almost perfect inter-rater agreement. The SAE developed for tooth preparation evaluation can be used for dental education and clinical skill feedback. SAE may minimize possible errors in the conventional rating and provide more reliable and precise assessments than the human-based DAE.

Indexed as

AlgorithmsSoftwareClinical CompetenceCrownsFeasibility StudiesHumans

Identifiers

PMID37481612
PMCPMC10363138

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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.