UNINTENTIONAL INJURY MORTALITY IN INDIA, 2005: NATIONALLY REPRESENTATIVE MORTALITY SURVEY OF 1.1 MILLION HOMES
Bibliographic record
Abstract
Background Unintentional injuries are an important cause of death in India. However, no reliable nationally representative estimates of unintentional injury deaths are available. Aim To estimate total unintentional injury mortality in India using results from a nationally representative survey of the causes of deaths. Methods Unintentional injury deaths were examined in a nationally representative mortality survey covering 123 000 deaths occurring in 1.1 million homes. Trained non-medical field staff to interview all households in which a death had occurred from 2001 to 2003 within its Sample Registration System. Structured field reports detailing the events preceding death were obtained from living relatives of the deceased and emailed to two of 130 trained physicians who independently assigned an underlying cause to each death. Cause specific mortality proportions were applied to all-cause mortality estimates from the United Nations for the year 2005. Results In the year 2005, unintentional injury caused 648 000 deaths (7% of all deaths; 58/100 000 population). Mortality rates were higher among males than females, and in rural versus urban areas. Road traffic injuries (185 000 deaths; 29% of unintentional injury deaths), falls (160 000 deaths, 25%) and drowning (73 000 deaths, 11%) were the three leading causes of unintentional injury mortality. The highest unintentional mortality rates were in those aged 70 years or older (410/100 000) mostly due to falls.
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How this classification was reachedexpand
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".