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Record W2067766751 · doi:10.1080/10255842.2013.766722

Finite element study of human lumbar disc nucleus replacements

2013· article· en· W2067766751 on OpenAlexaff
Hendrik Schmidt, Maxim Bashkuev, Fabio Galbusera, Hans‐Joachim Wilke, A. Shirazi‐Adl

Bibliographic record

VenueComputer Methods in Biomechanics & Biomedical Engineering · 2013
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsPolytechnique Montréal
FundersDeutsche Forschungsgemeinschaft
KeywordsNucleusFinite element methodMaterials sciencePoromechanicsLumbarBiomedical engineeringAnatomyComposite materialBiologyStructural engineeringMedicineNeuroscienceEngineeringPorosity

Abstract

fetched live from OpenAlex

Currently, there are a number of nucleus replacements under development. The important concern is how well these implants duplicate the mechanical function of the native nucleus. This finite element model study aimed to investigate the influence of different nucleus replacements on the mechanical response of the disc. Models included partial, full, over-sized, partially saturated, elastic and poroelastic solid replacements. Over-sized nucleus replacements up to 25% yielded results that were comparable to those in the intact state. Differences were much greater in cases with under-sized nucleus replacements. The effect was most pronounced for the 75% under-sized replacement that resembled the condition with a full nucleotomy. Nucleus implants with elastic properties substantially altered load transmission when 10% under-sized and over-sized replacements were considered. Compared to intact, the under-sized implants should be avoided when using biphasic materials with properties similar to the native nucleus, whereas for elastic replacements both under- and over-sized implants should not be used.

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)0.002
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.001

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 head0.033
GPT teacher head0.366
Teacher spread0.333 · 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

Citations29
Published2013
Admission routes1
Has abstractyes

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