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Record W2124442633 · doi:10.24095/hpcdp.33.3.03

Emergency department presentations for injuries associated with inflatable amusement structures, Canada, 1990-2009

2013· article· en· W2124442633 on OpenAlexaffvenueabout
SR McFaull, Glenn Keays

Bibliographic record

VenueChronic diseases and injuries in Canada · 2013
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMontreal Children's HospitalPublic Health Agency of Canada
Fundersnot available
KeywordsInflatableAmusementInjury surveillanceMedicineEmergency departmentMedical emergencyEpidemiologyEmergency medicineInjury preventionPoison controlEngineeringPsychologyNursingPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Inflatable amusement attractions, structures that are air-supported and inflated by a blower, have recently gained popularity. The purpose of this study was to describe the epidemiology of inflatable-related injuries presenting to Canadian emergency departments. METHODS: The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is an injury and poisoning surveillance system presently operating in the emergency departments of all 11 pediatric and 4 general hospitals across Canada. The CHIRPP was searched for cases of injuries associated with commercial inflatable amusement structures. RESULTS: Overall, 674 cases were identified over the 20-year surveillance period, during which time the average annual percent increase was 24.6% (95% CI: 21.6, 27.7). Children aged 2 to 9 years were the most frequently injured (59.3/100,000 CHIRPP cases), and fractures accounted for 34.5% of all injuries. DISCUSSION: A sharp increase in emergency department visits for injuries associated with commercial inflatable amusement structures has been observed in recent years. Injury mechanisms could be mitigated by product design modifications and stricter business operational practices.

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 score0.384
Threshold uncertainty score0.999

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)0.0010.000

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 head0.009
GPT teacher head0.267
Teacher spread0.258 · 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

Citations6
Published2013
Admission routes3
Has abstractyes

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