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Record W4392123225 · doi:10.3390/polym16050613

Evaluation of the Mechanical and Adhesion Characteristics of Indirect Restorations Manufactured with Three-Dimensional Printing

2024· article· en· W4392123225 on OpenAlexaboutno aff
Cem Peşkersoy, Aybeniz Oguzhan

Bibliographic record

VenuePolymers · 2024
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsnot available
FundersEge Üniversitesi
KeywordsCementation (geology)MolarBond strengthTemperature cyclingMaterials scienceDentistrySignificant difference3D printingOrthodonticsComposite materialAdhesiveMathematicsMedicinePhysicsCementThermal

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the marginal fit and bond strength characteristics of onlay restorations manufactured by three-dimensional printing (Varseo XS, Bego GmbH, Bremen, Germany) and CAD/CAM (CAMcube, Montreal, QC, Canada) systems. Class II onlay cavities on sixty mandibular molars were prepared in cavities and restored in three separate groups using different fabrication methods. Digital and conventional impressions were taken to design the restorations in the CAD system (DWOS, Straumann GmbH, Freiburg, Germany). To evaluate the marginal fit and void volumes, all specimens were scanned with microcomputed tomography. A microshear test was performed to compare the bond strength of the restorations to the tooth surface. The marginal fit values measured for the 3D-printed and CAD/CAM onlay restorations were found to be at clinically acceptable levels (<120 µm), and no significant difference could be observed between the three different fabrication methods (p > 0.05). According to the microshear test results, the CAD/CAM group had the highest bond strength values before (34.82 MPa) and after (26.87 MPa) thermal cycling (p < 0.05), while the 3D-printed and conventionally produced onlays had similar results (p < 0.05). 3D printing technology is a promising option for indirect restorations; however, the post-production phase is as crucial as the printing and cementation phases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.279
Teacher spread0.251 · 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 teacher head, 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

Citations8
Published2024
Admission routes1
Has abstractyes

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