Emergency department surveillance of injuries associated with bunk beds: the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), 1990–2009
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".