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Record W4210745362 · doi:10.1080/10255842.2022.2030730

Modeling attachment and compressive loading of locking and non-locking plate fixation: a finite element investigation of a supracondylar femur fracture model

2022· article· en· W4210745362 on OpenAlexaff
Amir Samiezadeh, Stewart McLachlin, Matthew Ng, Saeid Samiezadeh, Jérémie Larouche, Cari Whyne

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2022
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsHumber PolytechnicUniversity of WaterlooSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceFinite element methodFixation (population genetics)FemurTension (geology)Structural engineeringOrthodonticsComposite materialSurgeryCompression (physics)EngineeringMedicine

Abstract

fetched live from OpenAlex

This study developed a finite element (FE) model of simulated locking plate fixation to examine the strain response following supracondylar femoral plate attachment and under compressive loading. An implicit FE model of a synthetic femur with a distal fracture gap stabilized with a lateral plate was evaluated following attachment and 500 N loading, considering locking and non-locking proximal screws configurations. Screw pre-tension values of 60 N for both distal and proximal non-locking screws yielded good agreement with plate experimental strain data in attached (unloaded) and loaded conditions. The results highlight the importance of pre-tensioning in modeling plate attachment using non-locking screws.

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)0.000
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.026
GPT teacher head0.319
Teacher spread0.293 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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