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Record W2030381738 · doi:10.1097/brs.0b013e3182387d71

What Occupant Kinematics and Neuromuscular Responses Tell Us About Whiplash Injury

2011· review· en· W2030381738 on OpenAlexaff
Gunter P. Siegmund

Bibliographic record

VenueSpine · 2011
Typereview
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsAdvantage Forensics (Canada)
Fundersnot available
KeywordsWhiplashMedicinePhysical medicine and rehabilitationKinematicsPoison controlInjury preventionNeck musclesCrashPhysical therapyAnatomyMedical emergencyComputer science

Abstract

fetched live from OpenAlex

In Brief Study Design. Literature-based review. Objective. To review the published data on occupant kinematic and neuromuscular responses during low-speed impacts and analyze how these data inform our understanding of whiplash injury. Summary of Background Data. A stereotypical kinematic and neuromuscular response has been observed in human subjects exposed to rear-end impacts. Combined with various models of injury, these response data have been used to develop anti-whiplash seats that prevent whiplash injury in many, but not all, individuals exposed to a rear-end crash. Methods. Synthesis of the literature. Results. Understanding of the occupant kinematics and neuromuscular responses, combined with data from various seat-related interventions, have shown that differential motion between the superior and inferior ends of the cervical spine is responsible for many whiplash injuries. The number of whiplash injuries not prevented by current anti-whiplash seats suggests than further work remains, possibly related to designing seats that respond dynamically to the occupant and collision properties. Neck muscles alter the head and neck kinematics during the interval in which injury likely occurs, even in initially relaxed occupants. It remains unclear whether muscle activation mitigates or exacerbates whiplash injury. If muscle activation mitigates injury, then advance warning could be used to help occupant tense their muscles before impact. Alternatively, if muscle activation exacerbates whiplash injury, then a loud preimpact sound that uncouples the startle and postural components of the muscle response could reduce peak muscle activation during a whiplash exposure. Conclusion. Our improved understanding of whiplash injury has led to anti-whiplash seats that have prevented many whiplash injuries. Further work remains to optimize these and possibly other systems to further reduce the number of whiplash injuries. Detailed information of the occupant motion and muscle responses during a low-speed collision has shown that collision-induced forces and muscle-generated forces may interact to cause whiplash injury. Current anti-whiplash seats have successfully prevented many whiplash injuries, but many occupants are still injured. Improved seat designs and methods of potentially exploiting neck muscles to prevent neck injuries are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
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 head0.077
GPT teacher head0.367
Teacher spread0.290 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2011
Admission routes1
Has abstractyes

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