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Long-term response of femoral density to hip implant and bone fracture plate: Computational study using a mechano-biochemical model

2015· article· en· W2216617234 on OpenAlexafffund
Pouria Tavakkoli Avval, Saeid Samiezadeh, Habiba Bougherara

Bibliographic record

VenueMedical Engineering & Physics · 2015
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Metropolitan UniversityCentennial College
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFemurImplantStress shieldingBone remodelingMaterials scienceMedicineOrthodonticsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Although bone fracture plates can provide appropriate stability at the fracture site and lead to early patient mobilization, they significantly change the loading pattern in the bone after union (Stress shielding). This phenomenon results in a bone density decrease, which may cause premature failure of the implant. This paper presents the first study that quantifies the long-term response of femoral density to hip implantation and plating (lateral and anterior plating) using a mechano-biochemical model which considers the coupling effect between mechanical loading and biochemical affinities as stimuli for bone remodeling. The results showed that the regions directly beneath the plate experienced severe bone loss (i.e. up to ∼ -70%). However, some level of bone formation was observed in the vicinity of the most proximal and distal screw holes in both lateral and anterior plated femurs (i.e. up to ∼ +110%). The bone under the plate was divided into six zones. With respect to bone remodeling response, the findings revealed that anterior plating was not superior to lateral plating since the maximum and average bone losses among the zones in the anterior plated femur (i.e. -36% and -24%, respectively) were approximately the same as their corresponding values in the lateral plated femur (i.e. -38% and -24%, respectively).

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.609

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations14
Published2015
Admission routes2
Has abstractyes

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