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Development of a Biomimetic Metallic Femoral Stem: Methodological Approach

2016· article· en· W2554158645 on OpenAlexaff
Vladimir Braïlovski, Patrick Terriault, Charles Simoneau, Mathieu Dumas, Bruno Jetté

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsStress shieldingMaterials scienceReplicaFemurSelective laser meltingBiomedical engineeringPorosityFinite element methodPorous mediumDigital image correlationMechanical engineeringComposite materialImplantStructural engineeringSurgeryEngineeringMedicine

Abstract

fetched live from OpenAlex

In this communication, a new methodological approach is proposed to develop a biomimetic metallic femoral stem. The design of this stem starts with the definition of an outer skin by reproducing the shape and overall dimensions of a Stryker ® femoral stem to be implanted in an artificial femur model from Sawbones ® . In-house algorithms are then used to generate two types of porous structures inside the outer skin: either a stochastic cubic-based porous structure or an ordered diamond-type porous structure. Next, a model of the femur-stem assembly is developed using the finite element method. The fully dense Stryker stem replica and two porous stems are fabricated using selective laser melting technology. Then, comparative mechanical testing is carried out using the ISO 7206-4 (2010) guidelines. These tests are conducted on an intact artificial femur (reference case) and on the identical femurs, but now implanted with the fully dense and porous stems. Using digital image correlation tools, the results of four series of tests are compared to assess which implant design leads to the lowest stress shielding in the implanted femur. Finally, the experimentally measured strain fields are compared to the numerical predictions to validate the numerical models.

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)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.123
GPT teacher head0.332
Teacher spread0.209 · 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
Published2016
Admission routes1
Has abstractyes

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