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Record W1498256492 · doi:10.24095/hpcdp.33.2.06

Unintentional injury mortality and external causes in Canada from 2001 to 2007

2013· article· en· W1498256492 on OpenAlexaffvenueabout
Y. Chen, Fangfang Mo, QL Yi, Ying Jiang, Y Mao

Bibliographic record

VenueChronic diseases and injuries in Canada · 2013
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsPublic Health Agency of CanadaUniversity of Ottawa
FundersJohns Hopkins University
KeywordsDemographyMortality rateInjury preventionMedicinePoison controlOccupational safety and healthPopulationSuicide preventionEnvironmental healthSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: To understand the distribution pattern and time trend of unintentional injury mortalities is crucial in order to develop prevention strategies. METHODS: We analyzed vital statistics data from Canada (excluding Quebec) for 2001 to 2007. Mortality rates were age- and sex-standardized to the 2001 Canadian population. An autoregressive model was used for time-series analysis. RESULTS: Overall mortality rate steadily decreased but unintentional injury mortality rate was stable over the study period. The three territories had the highest mortality rates. Unintentional injury deaths were less common in children than in youths/adults. After 60, the mortality rate increased steadily with age. Males were more likely to die of unintentional injury, and the male/female ratio peaked in the 25- to 29-year age group. Motor vehicle crashes, falls and poisoning were the three major causes. There was a substantial year after year increase in mortality due to falls. Deaths due to motor vehicle crashes and drowning were more common in summer months, and deaths caused by falls and burns were more common in winter months. CONCLUSION: The share of unintentional injury among all-cause mortality and the mortality from falls increased in Canada during the period 2001 to 2007.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
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)0.0020.000

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 head0.015
GPT teacher head0.291
Teacher spread0.275 · 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.

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

Citations54
Published2013
Admission routes3
Has abstractyes

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