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A stability-based model of a growing spine with adolescent idiopathic scoliosis: A combination of musculoskeletal and finite element approaches

2019· article· en· W2910936266 on OpenAlexaff
Zeinab Kamal, Gholamreza Rouhi, Navid Arjmand, Samer Adeeb

Bibliographic record

VenueMedical Engineering & Physics · 2019
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsUniversity of Alberta
FundersAmirkabir University of Technology
KeywordsFinite element methodScoliosisIdiopathic scoliosisSPINE (molecular biology)Stability (learning theory)MedicineOrthodonticsPhysical medicine and rehabilitationStructural engineeringPhysical therapyComputer scienceEngineeringSurgeryBioinformaticsMachine learningBiology

Abstract

fetched live from OpenAlex

Using a combined musculoskeletal and finite element (FE) approach, this study aimed to evaluate stability-based muscle forces in a spine with adolescent idiopathic scoliosis (AIS) as compared to a normal spine; and subsequently, determine the effects of stress distribution on the growth plates (GPs) of the growing spine. For this purpose a nonlinear 3D FE model of one normal and one scoliotic thoracolumbar spine, consisting of GPs attached to rigid L1 to L4 vertebrae, were developed using computed tomography images coupled with a growth modulation using the Stokes' model. Corresponding well with recent in-vivo and in-vitro studies, results of the models predicted intradiscal pressures at the L3-L4 and L4-L5 levels of 0.32 and 0.38 MPa in the normal spine and 0.30 and 0.36 MPa in the scoliotic spine, respectively; and hydrostatic and octahedral shear stresses on the apical GP of 0.11 and 0.06 MPa, respectively. The reaction moments in the scoliotic model resulted in higher compression on the posteroconcave side of the GPs, which led to deformity progression as predicted by the Hueter-Volkmann theory. Moreover, the augmented baseline growth in the Stokes' model magnified both the scoliotic spine height and Cobb angle growth rates. The presented stability-based approach can be used to predict the performance of rehabilitation strategies in the clinical management of AIS.

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)0.000
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.232
Teacher spread0.208 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

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