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Record W2913916248 · doi:10.1080/10255842.2018.1563599

Development and validation of a finite element model to simulate the opening of a medial opening wedge high tibial osteotomy

2019· article· en· W2913916248 on OpenAlexafffund
Victor Alexander Carranza, Jacob M. Reeves, Alan Getgood, Timothy A. Burkhart

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2019
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsLawson Health Research InstituteFowler Kennedy Sport Medicine ClinicWestern University
FundersCanadian Institutes of Health ResearchSmith and Nephew
KeywordsHigh tibial osteotomyCadaveric spasmSagittal planeOsteotomyCoronal planeWedge (geometry)OsteoarthritisMedicineFinite element methodTibiaOrthodonticsBiomechanicsAnatomyMathematicsStructural engineeringGeometryEngineering

Abstract

fetched live from OpenAlex

Medial opening wedge high tibial osteotomy (MOWHTO) is a surgical procedure intended to alter the coronal and sagittal plane alignment of the lower limb to primarily relieve the symptoms of osteoarthritis in the medial compartment of the knee. The purpose of this work was to develop and validate a finite element model to simulate the opening of a high tibial osteotomy and determine whether a pilot hole at the cortical hinge reduces the risk of lateral cortical fracture. Fifteen models were reconstructed from CT images of eight cadaveric specimens. The validated models indicated that the addition of the pilot hole increased the stresses and likelihood of a type-I and type-II fractures during the opening of a medial open wedge high tibial osteotomy compared to the no-hole condition.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.319
Teacher spread0.292 · 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
GenreMethods

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
Published2019
Admission routes2
Has abstractyes

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