Road traffic injuries and associated mortality in the Islamic Republic of Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it