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Record W2223844799 · doi:10.1136/bmjopen-2014-007307

Burden and trend analysis of injury mortality in China among children aged 0–14 years from 2004 to 2011

2015· article· en· W2223844799 on OpenAlexaff
Zhaoxue Yin, Jing Wu, Jiesi Luo, Anita W. P. Pak, Bernard C. K. Choi, Xiaofeng Liang

Bibliographic record

VenueBMJ Open · 2015
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineInjury preventionChinaMortality rateDemographyPoison controlRural areaPublic healthEpidemiologyOccupational safety and healthEnvironmental healthSuicide preventionPediatricsSurgeryInternal medicineGeography

Abstract

fetched live from OpenAlex

OBJECTIVE: To track changes of the burden and trends of childhood injury mortality among children aged 0-14 years in China from 2004 to 2011. DESIGN: National representative data from the Chinese Disease Surveillance Points system and Chinese Maternal and Child Mortality Surveillance system from 2004 to 2011 were used. Rates and 95% CIs of aged-standardised mortality, as well as the proportions of injury death, were estimated. SETTING: Urban and rural China. PARTICIPANTS: Children aged 0-14 years from 2004 to 2011. RESULTS: The proportion of injury among all deaths in children increased from 18.69% in 2004 to 21.26% in 2011. A 'V' shape change was found in the age-standardised injury mortality rate during the study period among the children aged 0-14 years, with the age-standardised injury mortality rate decreasing from 29.71 per 100,000 per year in 2004 to 24.12 in 2007, and then increasing to 28.12 in 2011. A similar change was observed in the rural area. But the age-standardised mortality rate decreased consistently in the urban area. The rate was higher among boys than among girls. Drowning, road traffic accidents and falls were consistently the top three causes of death among children. CONCLUSIONS: Childhood injury is an increasingly serious public health problem in China. The increasing trend of childhood injury mortality is driven by the rural areas rather than urban areas. More effective strategies and measures for injury prevention and control are needed for rural areas, boys, drowning, road traffic accidents and falls.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.061
GPT teacher head0.419
Teacher spread0.358 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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