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Record W1977495808 · doi:10.1016/j.orthres.2005.04.002

Effect of trunk flexion on cervical muscle EMG to rear impacts

2005· article· en· W1977495808 on OpenAlexaff
Shrawan Kumar, Robert Ferrari, Yogesh Narayan

Bibliographic record

VenueJournal of Orthopaedic Research® · 2005
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTrunkWhiplashElectromyographyPhysical medicine and rehabilitationMedicineAnatomyMuscle contractionMotor unit recruitmentPhysical therapyPoison controlBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the effect of occupant positioning on the response of the cervical muscles to whiplash-type posterolateral impacts. METHODS: Twenty healthy volunteers underwent left posterolateral whiplash-type impacts with the volunteers seated "out-of-position". Electromyograms of the cervical muscles were recorded. RESULTS: Whether having the trunk flexed to the left or right at the time of impact, the muscle responses were low in magnitude, showing a trend to increasing EMG responses with increasing acceleration (P>0.05). The time to onset and time to peak electromyogram for most muscles showed a trend to progressively decrease with increasing levels of acceleration. With the subject flexed to the left, all muscles generated 31% or less of the maximal voluntary contraction electromyogram. With the subject flexed to the right, all muscles generated 27% or less of their maximal electromyogram. In both positions, the trapezii were the most active (P<0.05). Thus, having the trunk flexed out of neutral posture at the time of impact produces a very low magnitude cervical muscle response compared to impacts with the trunk in neutral posture. CONCLUSIONS: In the absence of bodily impact, the flexed trunk posture appears to produce a biomechanical response that would probably decrease the likelihood of cervical muscle injury in low velocity posterolateral impacts.

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)0.006
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.411
Teacher spread0.362 · 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 designOther design
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

Citations6
Published2005
Admission routes1
Has abstractyes

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