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Record W2131383841 · doi:10.1142/s021951941550061x

A THREE-DIMENSIONAL COMPUTER MODEL TO SIMULATE SPONGY BONE REMODELING UNDER OVERLOAD USING A SEMI-MECHANISTIC BONE REMODELING THEORY

2015· article· en· W2131383841 on OpenAlexafffund
Gholamreza Rouhi, Ali Vahdati, Xianjie Li, L. J. Sudak

Bibliographic record

VenueJournal of Mechanics in Medicine and Biology · 2015
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of CalgaryUniversity of Ottawa
FundersUniversity of Calgary
KeywordsBone remodelingOsteocyteBone resorptionBone remodeling periodResorptionFunction (biology)Biomedical engineeringMaterials scienceChemistryMedicineBiologyOsteoblastCell biologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Overload has been suggested as a contributing factor for bone loss, for instance at the bone implant interface. The objective of this study is to investigate spongy bone resorption under overload using a semi-mechanistic bone remodeling theory. Since overload can cause the accumulation of microdamage in bone, in this study, it is assumed that overload will increase the osteoclastic activity, and also will reduce the osteocyte influence distance. First, a previously proposed semi-mechanistic bone remodeling theory was extended by defining a new form for the resorption probability function, which is based on experimental evidence. Then, in order to investigate the validity of our hypothesis, a three-dimensional finite element model of spongy bone was developed. The simulation results show that, first, trabeculae adapt with the mechanical stimuli placed on them. Secondly, a sharp reduction in spongy bone density will be resulted, in agreement with experimental evidence, when bone is under overload.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.120
GPT teacher head0.350
Teacher spread0.230 · 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 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

Citations16
Published2015
Admission routes2
Has abstractyes

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