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Record W4319812932 · doi:10.1111/cid.13190

Effect of intraoral scanner and fixed partial denture situation on the scan accuracy of multiple implants: An in vitro study

2023· article· en· W4319812932 on OpenAlexvenueno aff
Mustafa Borga Dönmez, Ayse Mathey, Fabio Gäumann, Amber Mathey, Burak Yılmaz, Samir Abou‐Ayash

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsScannerPremolarImplantCERECComputer scienceMolarOrthodonticsBiomedical engineeringMaterials scienceMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Accuracy of intraoral implant scans may be affected by the region of the implant and the type of the intraoral scanner (IOSs). However, there is limited knowledge on the scan accuracy of multiple implants placed for an implant-supported fixed partial denture (FPD) in different partially edentulous situations when digitized by using different IOSs. PURPOSE: To investigate the effect of IOS and FPD situation on the scan accuracy of two implants when partial-arch scans were performed. MATERIALS AND METHODS: Tissue level implants were placed in 3 maxillary models with implant spaces either at right first premolar and right first molar sites (Model 1, 3-unit FPD), at right canine and right first molar sites (Model 2, 4-unit FPD), or at lateral incisor sites (Model 3, 4-unit FPD). Reference standard tessellation language (STL) files of the models were generated by using an optical scanner (ATOS Capsule 200MV120). Two IOSs (CEREC Primescan [CP] and TRIOS 3 [TR]) were used to perform partial-arch scans (test-scans) of each model (n = 14), which were exported in STL format. A metrology-grade analysis software (GOM Inspect 2018) was used to superimpose test-scan STLs over the reference STL to calculate 3D distance, inter-implant distance, and angular (mesiodistal and buccopalatal) deviations. Trueness and precision analyses were performed by using bootstrap analysis of variance followed by Welch tests with Holm correction (α = 0.05). RESULTS: Trueness of the scans was affected by IOS and FPD situation when 3D distance deviations were considered, while inter-implant distance, mesiodistal angular, and buccopalatal angular deviations were only affected by the FPD situation (p < 0.001). Scan precision was affected by the interaction between the IOSs and the FPD situation when 3D distance and buccopalatal angular deviations were concerned, while IOSs and FPD situation were effective when all deviations were concerned (p≤ 0.001). When 3D distance deviations were considered, CP scans had higher accuracy TR scans in Models 1 and 3 (p ≤ 0.002), and the Model 1 scans had the highest accuracy (p < 0.001). When inter-implant distance deviations were considered, Model 1 scans had the highest accuracy with CP and higher accuracy than Model 2 when TR was used (p ≤ 0.030). When mesiodistal angular deviations were considered, Model 1 scans had the highest accuracy (p ≤ 0.040). When buccopalatal angular deviations were considered, Model 1 scans had the highest accuracy among models when CP was used (p ≤ 0.020). CONCLUSIONS: Posterior 3-unit fixed partial denture implant scans, CP scans, and combination of these two factors had accuracy either similar to or better than their tested counterparts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.116
GPT teacher head0.486
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2023
Admission routes1
Has abstractyes

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