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Record W2132623656 · doi:10.1177/1010539511430252

Home and Other Nontraffic Injuries Among Children and Youth in a High-Income Middle Eastern Country

2011· article· en· W2132623656 on OpenAlexaff
Michal Grivna, Peter Barss, Cristina Stănculescu, Hani O. Eid, Fikri M. Abu‐Zidan

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2011
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMcGill UniversityUniversity of British ColumbiaInterior Health
Fundersnot available
KeywordsMedicineInjury preventionPoison controlIncidence (geometry)Occupational safety and healthSuicide preventionAbbreviated Injury ScalePediatricsHuman factors and ergonomicsDemographyInjury Severity ScoreMedical emergency

Abstract

fetched live from OpenAlex

A trauma registry in the United Arab Emirates was used to ascertain nontraffic injuries of 0- to 19-year-olds. The registry's value for prevention was assessed. A total of 292 children and youth with nontraffic injuries were admitted for >24 hours at surgical wards of the main trauma hospital in Al Ain region during 36 months in 2003-2006. Injuries were analyzed by external cause, location, body part, and severity. Nontraffic represented 60% (n = 292) of child and youth injuries. Incidence/100 000 person-years was 91 for males, 43 for females. Unintentional included falls 65% (n = 191), burns 17% (n = 49), animal-related (mainly camel) 3% (n = 10), and others 10% (n = 29). Intentional accounted for 4% (n = 13). Falls affected all ages, burns mainly 1- to 4-year-olds. Of the injuries, 70% occurred at home. Most frequent and severe injuries measured by the Injury Severity Score and Abbreviated Injury Scale involved extremities. Prevention of home falls for all ages and burns of 1- to 4-year-olds are priorities. Registries should cover pediatric wards and include data on fall locations and hazardous products.

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)0.004
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 score0.007
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 head0.044
GPT teacher head0.286
Teacher spread0.242 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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