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Record W4253721867 · doi:10.32920/ryerson.14649990.v1

A validated finite element study of stress shielding in a novel hybrid knee implant

2021· preprint· en· W4253721867 on OpenAlexaff
Ziauddin Mahboob

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
Fundersnot available
KeywordsFinite element methodCadaveric spasmFemurImplantMaterials scienceStress (linguistics)Stress shieldingBiomedical engineeringStructural engineeringOrthodonticsAnatomyEngineeringSurgeryMedicine

Abstract

fetched live from OpenAlex

This study (1) proposes a hybrid knee implant design to improve stress transfer to bone tissue in the distal femur by modifying a conventional femoral implant to include a layer of carbon fibre reinforced polyamide 12, and (2) develops a finite element model of the prosthetic knee joint, validated by comparison with a parallel experimental study. The Duracon knee system was used in the experimental study, and its geometry was modelled using CAD software. Synthetic bone replicas were used instead of cadaveric specimens in the experiments. The strains generated on the femur and implant surfaces were measured under axial compressive loads of 2000 N and 3000 N. A mesh of 105795 nodes was needed to obtain sufficient accuracy in the finite element model, which reproduced the experimental reading within 10-23% in six of the eight test locations. The model of the proposed hybrid design showed considerable improvements in stress transfer to the bone tissue at three test flexion angles of 0°, 20°, and 60°.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations7
Published2021
Admission routes1
Has abstractyes

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