Road traffic injury mortality and its mechanisms in India: nationally representative mortality survey of 1.1 million homes
Bibliographic record
Abstract
OBJECTIVES: To quantify and describe the mechanism of road traffic injury (RTI) deaths in India. DESIGN: We conducted a nationally representative mortality survey where at least two physicians coded each non-medical field staff's verbal autopsy reports. RTI mechanism data were extracted from the narrative section of these reports. SETTING: 1.1 million homes in India. PARTICIPANTS: Over 122 000 deaths at all ages from 2001 to 2003. PRIMARY AND SECONDARY OUTCOME MEASURES: Age-specific and sex-specific mortality rates, place and timing of death, modes of transportation and injuries sustained. RESULTS: The 2299 RTI deaths in the survey correspond to an estimated 183 600 RTI deaths or about 2% of all deaths in 2005 nationally, of which 65% occurred in men between the ages 15 and 59 years. The age-adjusted mortality rate was greater in men than in women, in urban than in rural areas, and was notably higher than that estimated from the national police records. Pedestrians (68 000), motorcyclists (36 000) and other vulnerable road users (20 000) constituted 68% of RTI deaths (124 000) nationally. Among the study sample, the majority of all RTI deaths occurred at the scene of collision (1005/1733, 58%), within minutes of collision (883/1596, 55%), and/or involved a head injury (691/1124, 62%). Compared to non-pedestrian RTI deaths, about 55 000 (81%) of pedestrian deaths were associated with less education and living in poorer neighbourhoods. CONCLUSIONS: In India, RTIs cause a substantial number of deaths, particularly among pedestrians and other vulnerable road users. Interventions to prevent collisions and reduce injuries might address over half of the RTI deaths. Improved prehospital transport and hospital trauma care might address just over a third of the RTI deaths.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".