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Record W1855545797 · doi:10.24095/hpcdp.33.1.05

Emergency department surveillance of injuries associated with bunk beds: the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), 1990–2009

2012· article· en· W1855545797 on OpenAlexaffvenueabout
SR McFaull, Mylène Fréchette, S. Rachel Skinner

Bibliographic record

VenueChronic diseases and injuries in Canada · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsEmergency departmentInjury surveillanceInjury preventionOccupational safety and healthPoison controlMedicineSuicide preventionMedical emergencyEmergency medicineNursingPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Due to space constraints, bunk beds are a common sleeping arrangement in many homes. The height and design of the structure can present a fall and strangulation hazard, especially for young children. The primary purpose of this study was to describe bunk bed-related injuries reported to the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), 1990-2009. METHODS: CHIRPP is an injury and poisoning surveillance system operating in 11 pediatric and 4 general emergency departments across Canada. Records were extracted using CHIRPP product codes and narratives. RESULTS: Over the 20-year surveillance period, 6002 individuals presented to Canadian emergency departments for an injury associated with a bunk bed. Overall, the frequency of bunk bed-related injuries in CHIRPP has remained relatively stable with an average annual percent change of 21.2% (21.8% to 20.5%). Over 90% of upper bunk-related injuries were due to falls and children 3-5 years of age were most frequently injured (471.2/100 000 CHIRPP cases). CONCLUSION: Children with bunk bed-related injuries continue to present to Canadian emergency departments, many with significant injuries. Injury prevention efforts should focus on children under 6 years of age.

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 score0.233
Threshold uncertainty score0.522

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 head0.011
GPT teacher head0.297
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

Citations17
Published2012
Admission routes3
Has abstractyes

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