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Record W2130555064 · doi:10.1186/1472-698x-12-17

The pattern of injury and poisoning in South East Iran

2012· article· en· W2130555064 on OpenAlexaff
Alireza Ansari‐Moghaddam, Alexandra Martiniuk, Mahdi Mohammadi, Mahdieh Rad, Fatemeh Sargazi, Khodadad Sheykhzadeh, Seddighe Jelodarzadeh, Fatemeh Karimzadeh

Bibliographic record

VenueBMC International Health and Human Rights · 2012
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Toronto
FundersZahedan University of Medical Sciences
KeywordsMedicineIncidence (geometry)Injury preventionEnvironmental healthPoison controlOccupational safety and healthSuicide preventionPublic healthRural areaRoad trafficDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Injury is a leading cause of morbidity and mortality worldwide, and even more so in low and middle-income countries (LMICs). Iran is a LMIC and lacks information regarding injury for program and policy purposes. This study aimed to describe the incidence and patterns of injury in one province in South Eastern Iran. METHODS: A hospital-based, retrospective case review using a routinely collected registry in all Emergency Departments in Sistan and Baluchistan province, Iran for 12 months in 2007-2008. RESULTS: In total 18,155 injuries were recorded during the study period. The majority of injuries in South Eastern Iran were due to road traffic crashes. Individuals living in urban areas sustained more injuries compared to individuals from rural areas. Males typically experienced more injuries than females. Males were most likely to be injured in a street/alley or village whereas females were most likely to be injured in or around the home. In urban areas, road traffic related injuries were observed to affect older age groups more than younger age groups. Poisoning was most common in the youngest age group, 0 to 4 years. CONCLUSIONS: This study provides data on incidence and patterns of injury in South Eastern Iran. Knowledge of injury burden, such as this paper, is likely to help policy makers and planners with health service planning and injury prevention.

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)0.001
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 score0.026
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.048
GPT teacher head0.378
Teacher spread0.330 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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