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Record W2161350334 · doi:10.1002/jbm.b.32500

Investigation of stress shielding around the Stryker Omnifit and Exeter periprosthetic hip implants using an irreversible thermodynamic‐based model

2011· review· en· W2161350334 on OpenAlexaff
Alireza Sayyidmousavi, Habiba Bougherara

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2011
Typereview
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStress shieldingPeriprostheticMaterials scienceBone remodelingFemurBiomedical engineeringImplantFinite element methodArthroplastyMedicineThermodynamicsSurgeryPhysics

Abstract

fetched live from OpenAlex

This study investigates stress shielding by predicting bone density around two different implants following total hip arthroplasty using a new thermodynamic-based model for bone remodeling. This model is based on chemical kinetics and irreversible thermodynamics in which bone is treated as a self-organizing system capable of exchanging matter, energy, and entropy with its surroundings. Unlike the previous works in which mechanical loading is regarded as the only stimulus for bone remodeling, this model establishes a coupling between mechanical loading and the chemical reactions involved in the process of bone remodeling. This model is incorporated into the finite element software ANSYS by means of a macro to investigate stress shielding around two different implants: Stryker Omnifit and Exeter periprosthetic hip stems. The results of the simulation showing bone density reductions of 17% in Gruen zone 1 and 27% in Gruen zones 7 around the Omnifit hip stem agree well with dual-energy X-ray absorptiometry (DEXA) measurements reported in the literature. On the other hand, the Exeter implant is found to result in more severe resorption in the proximal femur. This is consistent with clinical studies, which report a higher survivorship rate for HA-coated Omnifit hip stems.

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)0.006
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.235
GPT teacher head0.399
Teacher spread0.164 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

Explore more

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