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Evaluation of effects of selected factors on inter-vertebral fusion—a simulation study

2004· article· en· W2051316011 on OpenAlexafffund
Xiaobo Wang, Geneviève Dumas

Bibliographic record

VenueMedical Engineering & Physics · 2004
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStiffnessBone densitySubsidenceFusionMaterials scienceSpinal fusionBiomedical engineeringOsteoporosisGeologyMedicineComposite materialSurgery

Abstract

fetched live from OpenAlex

This study simulated the effects of inter-vertebral disc degeneration and bone density distribution on the structural stiffness and strength provided by inter-vertebral fusion. Based on the original and redistributed bone density distributions, the effects of selected factors, including contact area between device/graft and vertebral endplates, endplate conditions, and bone growth capacity were evaluated using a factorial design of experiment. The simulation results suggested that the degeneration of inter-vertebral disc significantly affected the bone density and density distribution in adjacent vertebrae. The mechanical strength immediately after instrumentation is the worst case of device/graft subsidence. After that procedure, bone will adapt itself to the changed loading conditions and therefore reduce the risk of subsidence. A deficiency in structural stiffness immediately after instrumentation could be the "worst-case scenario" depending on the combinations of selected factors. The simulation results demonstrated that the contact area and initial bone density distribution should be considered jointly while estimating the risk of device/graft subsidence. The endplate condition is a secondary factor on the subsidence risk, compared with the contact area and initial bone density distribution.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.023
GPT teacher head0.315
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 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

Citations14
Published2004
Admission routes2
Has abstractyes

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