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Record W4405825735 · doi:10.1142/s0218957724500222

THE IMPORTANCE OF LIGAMENT PROPERTIES IN CERVICAL SPINE KINEMATICS: A FINITE ELEMENT STUDY

2024· article· en· W4405825735 on OpenAlexafffund
Numaira Obaid, Brian K. Kwon, Carolyn J. Sparrey

Bibliographic record

VenueJournal of Musculoskeletal Research · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsVancouver Spine Surgery InstituteInternational Collaboration On Repair DiscoveriesSimon Fraser University
FundersMichael Smith Health Research BCSFU Community Trust Endowment FundNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCraig H. Neilsen Foundation
KeywordsKinematicsCervical spineLigamentFinite element methodOrthodonticsComputer scienceMedicineLibrary scienceAnatomySurgeryEngineeringPhysicsStructural engineering

Abstract

fetched live from OpenAlex

Rotation of the cervical spine beyond its normal range of motion is a leading cause of fall-related spinal cord injuries (SCIs) in older adults. This rotation is constrained, in part, by the spinal ligaments. The experimentally measured properties of these ligaments are tabulated in literature, including sex- specific properties; however, their influence on the rotation kinematics of the cervical spine has not been compared. We examined how different mechanical properties of spinal ligaments, including sex- specific properties, affected the rotational kinematics of the cervical spine using finite element analysis (FEA). Ligament properties most influenced the rotation of the lower cervical spine, with increased ligament stiffness reducing rotation. Ligament deformation remained mostly in the toe region of their force-displacement curves, emphasizing the need to incorporate non-linear ligament behavior in FEA. Predictions made using one set of experimental properties (Property 1) better-matched experimental kinematic data. Using sex-specific properties had a moderate effect (6% in extension, − 3% in flexion) on rotation, with a greater impact on extension. Ligament properties also affected the segmental distribution of rotation, causing a variability of 3–21% at different levels. We emphasized the need to incorporate tailored approaches to FEA to obtain clinically relevant results when modeling flexion/extension rotation.

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)0.005
metaresearch head score (Gemma)0.001
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 score0.437
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.071
GPT teacher head0.437
Teacher spread0.366 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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