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Computational Simulation of Mechanical Microenvironment of Early Stage of Bone Healing under Locking Compression Plate with Dynamic Locking Screws

2014· article· en· W2051959628 on OpenAlex

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Mechanics and Materials · 2014
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsRichmond Hospital
Fundersnot available
KeywordsBone healingMaterials scienceStiffnessFixation (population genetics)Process (computing)Biomedical engineeringCompression (physics)Computer scienceEngineeringMedicineSurgeryComposite material

Abstract

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It is well known that bone healing outcomes highly depend on the mechanical microenvironment of the fracture site, and a certain degree of interfragmentary movement (IFM) is essential for indirect (i.e. natural) bone healing. The application of locking compression plate (LCP) internal fixation in the treatment of bone fracture is a common practice which leads to early mobility and full function of the fractured extremity. However should the fixation configuration be too stiff, it might result in delayed healing or asymmetric tissue development across the fracture site due to the fact that IFM in near cortex area is too small to promote healing. Dynamic locking screw (DLS) has been recently designed to tackle this problem by reducing the stiffness of LCP fixation. However, the actual mechano-regulation mechanisms in which DLS uses to regulate the healing process are still not fully understood. The objective of this paper is to develop a computational model to understand the change of mechanical microenvironment of fracture site under LCP with dynamic locking screw in comparison to standard locking screw, and how this change could potentially regulate tissue development within the fracture callus during the healing process.Keywords: bone healing, locking compression plate, dynamic locking screw, finite element modelling

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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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.404

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 head0.012
GPT teacher head0.256
Teacher spread0.244 · 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