Epidemiology of Urban Traffic Accidents: A Study on the Victims’ Health Records in Iran
Bibliographic record
Abstract
INTRODUCTION: Several studies have been carried out in the field of traffic collisions in Iran. However, few studies have used the victims’ medical records as a source of information. This study aimed to use the data collected from the medical records and a geographic information system to show the epidemiology of urban traffic collisions to be used in injury prevention strategies. METHODS: This was a descriptive, cross-sectional study which was completed in 2013. The sample consisted of 1240 medical records of the people injured in the urban traffic collisions in the capital city of Iran between October 2010 and April 2011. Data were analyzed by using SPSS 18.0 and ARC GIS 10.0. RESULTS: According to the results, motorcyclists were the main group of victims, and most collisions occurred in the afternoon between 12:00 and 18:00 pm. Moreover, the findings showed that the frequency of collisions was higher in District five (16.7%), District six (13.7%), and District 12 (8.3%) of the city. CONCLUSION: In most traffic collisions, motorcyclists were involved and victims mainly suffered from injuries in the lower limbs. Therefore, training in the use of safety equipment, setting collision prevention strategies, and controlling the risky behavior of motorcyclists may help to reduce the number of collisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".