Road traffic injuries and associated mortality in the Islamic Republic of Iran
Bibliographic record
Abstract
Background: Road traffic accidents are a major public health problem globally, causing millions of injuries, deaths and disabilities, and a huge loss of financial resources, especially in low- and middle-income countries. Aim: To determine the incidence of road traffic injuries and associated mortality from 1997 to 2020 in the Islamic Republic of Iran. Methods: This retrospective study used data from the Legal Medicine Organization of the Islamic Republic of Iran to estimate the annual rates of road traffic injuries and associated mortality from 21 March 1997 to 20 March 2020. The data were analysed using STATA version 14 and the annual rates are reported per 100 000 population. Results: During the study period, 5 760 835 road traffic injuries and 472 193 deaths were recorded in the Islamic Republic of Iran. The mortality rate increased from 22.4 per 100 000 in 1997 to 40 per 100 000 in 2005 and decreased to 18.4 per 100 000 in 2020. The injury rate increased from 111.1 per 100 000 in 1997 to 394.9 per 100 000 in 2005. It decreased in 2006 and 2007 and increased from then until 2010, finally reaching 331.8 per 100 000 in 2020. The male to female ratio for road traffic mortality was 3.9 in 1997 and 4.6 in 2020. The case fatality rate was highest (20.1%) in 1997 and decreased to 5.6% in 2020. Conclusion: Continuous interventions are needed to reduce the burden of road traffic injuries and associated mortality in the Islamic Republic of Iran.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".