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Record W4285732822 · doi:10.3390/ma15144953

Stress Distribution Pattern in Zygomatic Implants Supporting Different Superstructure Materials

2022· article· en· W4285732822 on OpenAlexaff
Artak Heboyan, Roberto Lo Giudice, Les Kalman, Muhammad Sohail Zafar, João Paulo Mendes Tribst

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsWestern University
Fundersnot available
KeywordsSuperstructureMaterials scienceStress (linguistics)Composite materialOrthodonticsStructural engineeringEngineeringMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to assess and compare the stress-strain pattern of zygomatic dental implants supporting different superstructures using 3D finite element analysis (FEA). A model of a tridimensional edentulous maxilla with four dental implants was designed using the computer-aided design (CAD) software. Two standard and two zygomatic implants were positioned to support the U-shaped bar superstructure. In the computer-aided engineering (CAE) software, different materials have been simulated for the superstructure: cobalt-chrome (CoCr) alloy, titanium alloy (Ti), zirconia (Zr), carbon-fiber polymers (CF) and polyetheretherketone (PEEK). An axial load of 500 N was applied in the posterior regions near the zygomatic implants. Considering the mechanical response of the bone tissue, all superstructure materials resulted in homogeneous strain and thus could reconstruct the edentulous maxilla. However, with the aim to reduce the stress in the zygomatic implants and prosthetic screws, stiffer materials, such Zr, CoCr and Ti, appeared to be a preferable option.

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 categoriesInsufficient payload (model declined to judge)
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 score0.200
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0110.000

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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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.

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

Citations28
Published2022
Admission routes1
Has abstractyes

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