Road traffic injuries in Baghdad from 2003 to 2014: results of a randomised household cluster survey
Bibliographic record
Abstract
INTRODUCTION: Around 50 million people are killed or left disabled on the world's roads each year; most are in middle-income cities. In addition to this background risk, Baghdad has been plagued by decades of insecurity that undermine injury prevention strategies. This study aimed to determine death and disability and household consequences of road traffic injuries (RTIs) in postinvasion Baghdad. METHODS: A two-stage, cluster-randomised, community-based household survey was performed in May 2014 to determine the civilian burden of injury from 2003 to 2014 in Baghdad. In addition to questions about household member death, households were interviewed regarding crash specifics, healthcare required, disability, relatedness to conflict and resultant financial hardship. RESULTS: Nine hundred households, totalling 5148 individuals, were interviewed. There were 86 RTIs (16% of all reported injuries) that resulted in 8 deaths (9% of RTIs). Serious RTIs increased in the decade postinvasion and were estimated to be 26 341 in 2013 (350 per 100 000 persons). 53% of RTIs involved pedestrians, motorcyclists or bicyclists. 51% of families directly affected by a RTI reported a significant decline in household income or suffered food insecurity. CONCLUSIONS: RTIs were extremely common and have increased in Baghdad. Young adults, pedestrians, motorcyclists and bicyclists were the most frequently injured or killed by RTCs. There is a large burden of road injury, and the families of road injury victims suffered considerably from lost wages, often resulting in household food insecurity. Ongoing conflict may worsen RTI risk and undermine efforts to reduce road traffic death and disability.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".