Unintentional injury mortality in India, 2005: Nationally representative mortality survey of 1.1 million homes
Bibliographic record
Abstract
Unintentional injuries are an important cause of death in India. However, no reliable nationally representative estimates of unintentional injury deaths are available. Thus, we examined unintentional injury deaths in a nationally representative mortality survey.Trained field staff interviewed a living relative of those who had died during 2001-03. The verbal autopsy reports were sent to two of the 130 trained physicians, who independently assigned an ICD-10 code to each death. Discrepancies were resolved through reconciliation and adjudication. Proportionate cause specific mortality was used to produce national unintentional injury mortality estimates based on United Nations population and death estimates.In 2005, unintentional injury caused 648,000 deaths (7% of all deaths; 58/100,000 population). Unintentional injury mortality rates were higher among males than females, and in rural versus urban areas. Road traffic injuries (185,000 deaths; 29% of all unintentional injury deaths), falls (160,000 deaths, 25%) and drowning (73,000 deaths, 11%) were the three leading causes of unintentional injury mortality, with fire-related injury causing 5% of these deaths. The highest unintentional mortality rates were in those aged 70 years or older (410/100,000).These direct estimates of unintentional injury deaths in India (0.6 million) are lower than WHO indirect estimates (0.8 million), but double the estimates which rely on police reports (0.3 million). Importantly, they revise upward the mortality due to falls, particularly in the elderly, and revise downward mortality due to fires. Ongoing monitoring of injury mortality will enable development of evidence based injury prevention programs.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".