MétaCan
Menu
Back to cohort
Record W1905511842 · doi:10.1520/jfs15511j

Driver and Front Seat Passenger Fatalities Associated with Air Bag Deployment. Part 2: A Review of Injury Patterns and Investigative Issues

2002· review· en· W1905511842 on OpenAlexaff
MJ Shkrum, KJ McClafferty, ES Nowak, A German

Bibliographic record

VenueJournal of Forensic Sciences · 2002
Typereview
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsTransport CanadaLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsAeronauticsSoftware deploymentFront (military)Forensic engineeringEngineeringPoison controlMedical emergencyComputer securityTransport engineeringComputer scienceMedicineMechanical engineering

Abstract

fetched live from OpenAlex

Assessment of the role of air bag deployment in injury causation in a crash of any severity requires analysis of occupant, vehicle, and impact data. The potential injurious role of an air bag is independent of crash severity and is more obvious in minor collisions, particularly those involving "out-of-position" occupants. Factors such as occupant height and other constitutional and medical factors, intoxication, age, type, and proper use of other restraint systems, pre-impact braking and multiple impacts can contribute to an occupant being "out-of-position." Two injury mechanisms are described in out-of-position occupants: "punch-out" when the individual covers the air bag module before deployment and "membrane-force" when the occupant contacts a partly deployed air bag. Each mechanism is associated with injury patterns. In adults, "punch-out" can cause thoraco-abdominal trauma and "membrane-force" loading can lead to craniocervical injury. This can also occur in short-statured occupants including children subjected to both types of loading. In more severe collisions, other factors, e.g., intrusion, steering column and seatbelt loading and other occupant compartment contacts, can contribute to trauma.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.868
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.082
GPT teacher head0.339
Teacher spread0.257 · 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
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

Citations20
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Forensic SciencesSame topicAutomotive and Human Injury BiomechanicsFrench-language works237,207