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Record W2897646574 · doi:10.1115/1.4041702

Plastic Energy Dissipation in Lumbar Spine Implants: A Contact Mechanics Point of View

2018· article· en· W2897646574 on OpenAlexaff
Mohammad Hodaei, Ali Bahari, Pooneh Maghoul, V. Rabbani

Bibliographic record

VenueJournal of Engineering and Science in Medical Diagnostics and Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDissipationMaterials scienceMechanicsPlasticityAsperity (geotechnical engineering)ImplantGaussianLumbarLumbar spineSurface roughnessContact areaComposite materialPhysicsAnatomySurgeryMedicineThermodynamics

Abstract

fetched live from OpenAlex

In this study, an elastoplastic contact model is developed for L1–L5 lumbar spine implants. Roughness effect is included to estimate energy loss which is an indication of wear and subsequently the issue of metal debris in body. A Gaussian function is assumed for the distribution of asperities. The contact surfaces of the implants are assumed to be spherical caps. Subsequently, a least-square approach is applied to obtain an approximate expression for the contact force using the data from integration over contact zone. The energy loss is calculated, next, which is due to plastic deformations of asperities. The numerical results indicate that for a given loading–unloading condition, the amount of energy dissipation increases in L1–L4 lumbar spine implants, while it decreases from L4 to L5 implants. The implants geometrical specifications are chosen to cover a wide range of patients' age. Finally, a closed-form expression is obtained for the plastic energy dissipation per cycle in terms of plasticity index for the lumbar spine L4, as the worst-case scenario. Such a function can serve as a very useful tool for implant designers and manufacturers.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.009
GPT teacher head0.265
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2018
Admission routes1
Has abstractyes

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