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Record W1969393961 · doi:10.1136/ebn.3.4.128

Restraint use and rear seating were associated with fewer serious injuries and deaths for children in motor vehicle crashes

2000· article· en· W1969393961 on OpenAlexaff
Nancy Edwards

Bibliographic record

VenueEvidence-Based Nursing · 2000
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineCrashMotor vehicle crashPediatricsPoison controlInjury preventionEmergency medicine

Abstract

fetched live from OpenAlex

Berg MD, Cook L, Corneli HM, et al . Effect of seating position and restraint use on injuries to children in motor vehicle crashes. Pediatrics2000 Apr; 105 : 831 –5. [OpenUrl][1][Abstract/FREE Full Text][2] QUESTION: Are restraint use and rear seating position associated with lower risks of serious injuries or death for children in motor vehicle crashes? Cohort study. Utah, USA. 5751 children <15 years of age who were passengers in a motor vehicle crash resulting in property damage over US$750 and fatality, admission to hospital, injuries with broken bones, or significant bleeding to any automobile occupant. Children travelling in passenger cars, light trucks, and vans were included. Children were between the ages of birth and 4 years (n=2016), 5 and 11 years (n=2231), and 12 and 14 years (n=1504). For the years 1992–6, motor vehicle crash records from a statewide database, including data on age, restraint use, type of crash, and front or rear seating in the vehicle, were linked by probabilistic methods to hospital discharge records. Restraint use was classified as optimal (a safety seat for ages … [1]: {openurl}?query=rft.jtitle%253DPediatrics%26rft.stitle%253DPediatrics%26rft.aulast%253DBerg%26rft.auinit1%253DM.%2BD.%26rft.volume%253D105%26rft.issue%253D4%26rft.spage%253D831%26rft.epage%253D835%26rft.atitle%253DEffect%2Bof%2BSeating%2BPosition%2Band%2BRestraint%2BUse%2Bon%2BInjuries%2Bto%2BChildren%2Bin%2BMotor%2BVehicle%2BCrashes%26rft_id%253Dinfo%253Adoi%252F10.1542%252Fpeds.105.4.831%26rft_id%253Dinfo%253Apmid%252F10742328%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=pediatrics&resid=105/4/831&atom=%2Febnurs%2F3%2F4%2F128.atom

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)0.001
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 score0.065
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.032
GPT teacher head0.318
Teacher spread0.286 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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