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Record W4243738153 · doi:10.32920/ryerson.14647719

A biomechanical study of a novel biomimetic hip implant

2021· preprint· en· W4243738153 on OpenAlexaff
Shaheen Khurshid

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsStress shieldingvon Mises yield criterionMaterials scienceComposite numberStiffnessStrain gaugeStress (linguistics)Composite materialFinite element methodImplantStructural engineeringMedicineSurgeryEngineering

Abstract

fetched live from OpenAlex

A three dimensional finite element (FE) model of a novel carbon fibre polyamide 12 composite hip stem was used to compare with two commerically available (Exeter and Omnifit) hip stems to minimize stress shielding and bone resorption. A virtual axial load of 3000N was applied to the FE model which replicated the experimental study. Strain and stress distributions were computed and compared with experimental results. Experimentally, three hip stems had their distal portions rigidly mounted and had strain gauges placed along the surface at 3 medial and 3 lateral locations. From the FE analysis, the von mises stress range for the composite hip stem was 200% and 45% lower than that in the Omnifit and Exeter implants, respectively. The aggregate average difference between FE and experimental microstrains for four proximal strain gauge locations were 7.5% (composite), 11.5% (Exeter), 14.6% (Omnifit), and the composite hip stem's stiffness (1982N/mm) was lower than the metallic hip stem stiffnesses (Exter, 2460N/mm; Omnifit, 2543 N/mm). This study showed considerable improvement in stress transfer to bone tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
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 science0.0000.000
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 head0.051
GPT teacher head0.316
Teacher spread0.265 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

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