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Record W2095707404 · doi:10.5539/gjhs.v5n3p115

In-vivo Kinematics of the Cervical Spine in Frontal Sled Tests

2013· article· en· W2095707404 on OpenAlexvenueno aff
Christoph Dehner, Sylvia Schick, Wolfram Hell, Péter Richter, Michael Kraus, Michael D. Kramer

Bibliographic record

VenueGlobal Journal of Health Science · 2013
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsnot available
Fundersnot available
KeywordsKinematicsCervical spineSPINE (molecular biology)In vivoMedicinePhysical medicine and rehabilitationSurgeryPhysicsBiologyBioinformatics

Abstract

fetched live from OpenAlex

The description of cervical spine motion and the risk to sustain a cervical spine injury in traffic accidents is mainly based on rear-end collisions. The knowledge about frontal collisions is comparable low. Therefore the objective of this exploratory study was, to describe the in-vivo cervical spine motion and acceleration during simulated frontal sled collisions and to identify sequences of motion in which the risk of injury is increased. A frontal collision with a speed change of 10.2km/h was simulated in a sled test with ten volunteers. Cervical spine kinematics was assessed by the simultaneous analysis of the angular head motion and acceleration as well as the simultaneous analysis of the relative motion and acceleration between the head and the first thoracic vertebral body. The motion sequence was divided into five phases. The combination of peak values of the angular head acceleration to ventral and the relative horizontal head acceleration to dorsal between the time period of 90ms and 110ms (early flexion phase) included - potential injury generating - shear forces. Although a hyperflexion (late rebound phase) as injury pattern didn't occur, dorsal soft tissue injuries due to eccentric muscle-sprain could not be ruled out completely. In conclusion the study showed under simulated test conditions that during the early flexion phase and the late rebound phase, acceleration and movement pattern occur that could lead to cervical spine injuries.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.180

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.341
Teacher spread0.320 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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