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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 OpenAlex
Amir Samiezadeh, Stewart McLachlin, Matthew Ng, Saeid Samiezadeh, Jérémie Larouche, Cari Whyne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

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

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.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.379
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.026
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