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Record W1989298764 · doi:10.1115/1.2241685

Simulation of Impact Test for Determining “Health” of Percutaneous Bone Anchored Implants

2006· article· en· W1989298764 on OpenAlexaff
S Jones, Gary Faulkner, Don Raboud, K.R. Fyfe, Johan Wolfaardt

Bibliographic record

VenueJournal of Biomechanical Engineering · 2006
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsMisericordia Community HospitalCochraneUniversity of Alberta
Fundersnot available
KeywordsOsseointegrationAbutmentImplantSoft tissueMaterials scienceBiomedical engineeringDentistryConnective tissueMedicineOrthodonticsSurgeryEngineering

Abstract

fetched live from OpenAlex

There is an ongoing requirement for a clinically relevant, noninvasive technique to monitor the integrity of percutaneous implants used for dental restorations, bone-anchored hearing aids, and to retain extra-oral prostheses (ear, eye, nose, etc). Because of the limitations of conventional diagnostic techniques (CT, MRI), mechanical techniques that measure the dynamic response of the implant-abutment system are being developed. This paper documents a finite element analysis that simulates a transient response to mechanical impact testing using contact elements. The detailed model allows for a specific interface between the implant and bone and characterizes potential clinical situations including loss of bone margin height, loss of osseointegration, and development of a soft connective tissue layer at the bone-implant interface. The results also show that the expected difference in interface stiffness between soft connective tissue and osseointegrated bone will cause easily measurable changes in the response of the implant/abutment system. With respect to the loss of bone margin height, changes in the order of 0.2 mm should be detectable, suggesting that this technique is at least as sensitive as radiography. A partial loss of osseointegration, while not being as readily evident as a bone margin loss, would still be detectable for losses as small as 0.5 mm.

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 score0.646
Threshold uncertainty score0.437

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)0.0000.000

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 head0.018
GPT teacher head0.320
Teacher spread0.302 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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