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Mechanisms of Pediatric Trauma Deaths in Canada and the United States: The Role of Firearms

2004· article· en· W2081988004 on OpenAlexaffabout
David J. Hackam, Mark V. Mazzioti, Richard H. Pearl, Gretchen M. Mazziotti, Andrea Winthrop, Jacob C. Langer

Bibliographic record

VenueThe Journal of Trauma: Injury, Infection, and Critical Care · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineDemographyInjury preventionSocioeconomic statusPopulationPoison controlOccupational safety and healthSuicide preventionLegislationEnvironmental healthGun controlMedical emergencyLawPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to determine whether firearms are a more prevalent cause of pediatric death in the United States than in Canada. METHODS: All pediatric trauma deaths from 1991 to 1996 in Ontario and Missouri were reviewed. Socioeconomic data were compiled for the two jurisdictions. RESULTS: During the period reviewed, there were 1,146 pediatric trauma deaths in Ontario (10.4 per 100,000 population) and 1,782 in Missouri (32.4 per 100,000 population). Firearm injuries accounted for 19% of the trauma deaths in Missouri and 0.5% of such deaths in Ontario. Overall, a child was 100 times more likely to die of firearm injury in Missouri (6 per 100,000 population) than in Ontario (0.06 per 100,000 population). The incidences of violent acts unrelated to firearms were similar between the two groups. Both populations were similar in terms of socioeconomic and education parameters, but differed in their rates for guns carried. CONCLUSION: The significantly higher death rate from firearm injuries in Missouri likely reflects differing gun control attitudes and legislation, and provides a rationale for prevention and future investigation.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.018
GPT teacher head0.314
Teacher spread0.296 · 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 designQualitative
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

Citations13
Published2004
Admission routes2
Has abstractyes

Explore more

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