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Biomechanical design of a new percutaneous locked plate for comminuted proximal tibia fractures

2022· article· en· W4223994293 on OpenAlexafffund
Aleksandar Djuricic, Aaron Gee, Emil H. Schemitsch, Cheryl E. Quenneville, Radovan Zdero

Bibliographic record

VenueMedical Engineering & Physics · 2022
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsWestern UniversityToronto Metropolitan UniversityVictoria HospitalMcMaster University
FundersDepartment of SurgeryWestern University
KeywordsStress shieldingMaterials scienceTibiaStiffnessUltimate tensile strengthComposite materialStress (linguistics)BiomechanicsOrthodonticsImplantSurgeryMedicineAnatomy

Abstract

fetched live from OpenAlex

Comminuted proximal tibia fractures are an ongoing surgical challenge. This “proof of concept” study is the first step in designing a new percutaneous plate for this injury under toe-touch weight-bearing as prescribed after surgery. Finite element simulations generated design curves for overall stiffness, bone and implant stress, and interfragmentary motion using 3 fixations (no, 1, or 2 “kickstand” (KS) screws across the fracture gap) over a range of plate elastic moduli (EP = 5 to 200 GPa). Combining well-established optimization criteria to enhance callus formation (i.e. 0.2 mm ≤ axial interfragmentary motion ≤ 1 mm; shear / axial interfragmentary motion ratio < 1.6), lessen stress shielding (i.e. bone stress under the proposed plate > bone stress under a traditional titanium or steel plate), and reduce steel screw breakage (i.e. screw max stress < ultimate tensile stress of steel) resulted in plate design recommendations: 172.6 ≤ EP < 200 GPa (no KS screw), 79.8 ≤ EP < 100 GPa (1 KS screw), and 4.9 ≤ EP < 100 GPa (2 KS screws). A prototype plate could be made from materials currently used or proposed for orthopaedics, such as polymers, fiber-reinforced polymers, fiber metal laminates, metal foams, or shape memory alloys.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.806
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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 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

Citations9
Published2022
Admission routes2
Has abstractyes

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