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Ejections of Young Children in Motor Vehicle Crashes

2003· article· en· W2035183205 on OpenAlexaffabout
Andrew Howard, A Moses McKeag, Linda Rothman, Jean-Louis Comeau, Brian C. Monk, Alan German

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2003
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsOntario Neurotrauma FoundationHospital for Sick ChildrenSickKids FoundationConference Board of Canada
Fundersnot available
KeywordsRollover (web design)ToddlerCrashInjury preventionOccupational safety and healthHuman factors and ergonomicsPoison controlSuicide preventionEnvironmental healthMedicineForensic engineeringEngineeringPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to report the incidence of ejection from the vehicle among children involved in motor vehicle crashes, and to describe a novel mode of ejection from child safety seats. METHODS: The U.S. National Automotive Sampling System General Estimates System and the Fatality Analysis Reporting System databases from 1995 through 1999 were analyzed. A prospective two-center study of children involved in severe car crashes in Canada was performed. RESULTS: Only 0.2% of 5.5 million children involved in crashes experienced ejection, but 1924 (29%) of 6570 child fatalities involved ejections. Only 2.2% of children experienced rollover crashes, but these contributed 1832 (28%) of 6570 child passenger fatalities. Among 56 crashes, 5 restrained young children were ejected, 4 in rollover crashes. Ejection of a toddler through the shoulder straps of a forward-facing child safety seat was the mechanism of ejection in three of the five cases. CONCLUSION: Ejection from the vehicle is common (29%) among fatally injured children. Shoulder straps alone (as found in T-shield or overhead shield child seats) may not prevent the ejection of toddlers from child safety seats during rollovers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.298
Teacher spread0.283 · 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

Citations29
Published2003
Admission routes2
Has abstractyes

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