MétaCan
Menu
Back to cohort
Record W4401387208 · doi:10.3390/coatings14080995

Zirconia Implants: A Brief Review and Surface Analysis of a Lost Implant

2024· review· en· W4401387208 on OpenAlexaff
Eduardo Borie, Eduardo Rosas, Raphael Freitas de Souza, Fernando José Días

Bibliographic record

VenueCoatings · 2024
Typereview
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsCubic zirconiaOsseointegrationMaterials scienceTitaniumZirconiumImplantScanning electron microscopeDentistryComposite materialMedicineMetallurgySurgeryCeramic

Abstract

fetched live from OpenAlex

Zirconia implants have emerged as a valuable alternative for clinical scenarios where aesthetic demands are high, as well as in cases of hypersensitivity to titanium or for patients who refuse metallic objects in their bodies due to personal reasons. However, these implants have undergone various changes in geometry, manufacturing techniques, and surface modifications since the introduction of the first zirconia implants. The present study aims to review the current evidence on zirconia implants, considering the changes they have undergone in recent years. Additionally, it aims to analyze the three-dimensional surface characteristics of a failed zirconia implant using scanning electron microscopy and elemental analysis with energy-dispersive X-ray spectrometry (EDX). A zirconia implant lost three weeks after placement was immediately assessed using VP-SEM equipment and chemically analyzed by EDX using a 410-M detector connected to the microscope. Sparse material depositions were found on all parts of the implant, with a notable concentration in the thread grooves. The elements identified in the sample included zirconium, oxygen, carbon, calcium, and phosphorus. This report demonstrates that the surface of zirconia implants can accumulate elements early in the process of bone matrix neoformation, which is consistent with the initial stage of osseointegration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.385
Teacher spread0.338 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCoatingsSame topicDental Implant Techniques and OutcomesFrench-language works237,207