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Record W1966424254 · doi:10.1115/1.4028107

High Rotation Rate Behavior of Cervical Spine Segments in Flexion and Extension

2014· article· en· W1966424254 on OpenAlexafffund
J. Barker, Duane S. Cronin, Naveen Chandrashekar

Bibliographic record

VenueJournal of Biomechanical Engineering · 2014
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRotation (mathematics)Cervical spineCervical vertebraeStiffnessBiomechanicsCervical spine injuryMaterials scienceSimulationStructural engineeringOrthodonticsBiomedical engineeringMathematicsComputer scienceAnatomyMedicineEngineeringGeometrySurgery

Abstract

fetched live from OpenAlex

Numerical finite element (FE) models of the neck have been developed to simulate occupant response and predict injury during motor vehicle collisions. However, there is a paucity of data on the response of young cervical spine segments under dynamic loading in flexion and extension, which is essential for the development or validation of tissue-level FE models. This limitation was identified during the development and validation of the FE model used in this study. The purpose of this study was to measure the high rotation rate loading response of human cervical spine segments in flexion and extension, and to investigate a new tissue-level FE model of the cervical spine with the experimental data to address a limitation in available data. Four test samples at each segment level from C2-C3 to C7-T1 were dissected from eight donors and were tested to 10 deg of rotation at 1 and 500 deg/s in flexion and extension using a custom built test apparatus. There was strong evidence (p < 0.05) of increased stiffness at the higher rotation rate above 4 deg of rotation in flexion and at 8 deg and 10 deg of rotation in extension. Cross-correlation software, Cora, was used to evaluate the fit between the experimental data and model predictions. The average rating was 0.771, which is considered to demonstrate a good correlation to the experimental data.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 designBench or experimental
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
Published2014
Admission routes2
Has abstractyes

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