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Record W2783356250 · doi:10.1097/pec.0000000000001395

Pediatric Electric Bicycle Injuries

2018· article· en· W2783356250 on OpenAlexaff
Karin Hermon, Tali Capua, Miguel Glatstein, Dennis Scolnik, Oren Tavor, Ayelet Rimon

Bibliographic record

VenuePediatric Emergency Care · 2018
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineEmergency departmentPoison controlInjury preventionRetrospective cohort studyOccupational safety and healthObservational studyEmergency medicineTrauma centerMedical emergencySuicide preventionHuman factors and ergonomicsSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Electric bicycles (E-bikes) are one of a wide range of light electric vehicles that provide convenient local transportation and attractive recreational opportunities. The aim of this study was to report E-bike-related injuries in children presenting to a trauma center. METHODS: Retrospective observational study, from December 2014 to November 2015, which included all pediatrics patients admitted to the emergency department with an injury related to E-bike use, was performed. RESULTS: A total of 97 E-bike injuries presented to the emergency department during this period. Mean age of E-bikers was 13.7 years (range, 7.5-16 years). Injuries to the head and the upper and the lower extremities were the most common. Thirteen patients (15%) were admitted, and 4 underwent surgery. CONCLUSIONS: Children are mainly injured as riders when using E-bikes. There is a need for regulation regarding the use of E-bikes to enhance the safety of both bikers and other road and pavement users.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.321
Teacher spread0.307 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2018
Admission routes1
Has abstractyes

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