Characterising violent deaths of undetermined intent: a population-based study, 1999–2012
Bibliographic record
Abstract
OBJECTIVES: Violent deaths classified as undetermined intent (UD) are sometimes included in suicide counts. This study investigated age and sex differences, along with socioeconomic gradients in UD and suicide deaths in the province of Ontario between 1999 and 2012. METHODS: We used data from the Institute for Clinical Evaluative Sciences, which has linked vital statistics from the Office of the Registrar General Deaths register with Census data between 1999 and 2012. Socioeconomic status was operationalised through the four dimensions of the Ontario Marginalization Index. We computed age-specific and annual age-standardised mortality rates, and risk ratios to calculate risk gradients according to each of the four dimensions of marginalization. RESULTS: Rates of UD-classified deaths were highest for men aged 45-64 years residing in the most materially deprived (7.9 per 100 000 population (95% CI 6.8 to 9.0)) and residentially unstable (8.1 (95% CI 7.1 to 9.1)) neighbourhoods. Similarly, suicide rates were highest among these same groups of men aged 45-64 living in the most materially deprived (28.2 (95% CI 26.1 to 30.3)) and residentially unstable (30.7 (95% CI 28.7 to 32.6)) neighbourhoods. Relative to methods of death, poisoning was the most frequently used method in UD cases (64%), while it represented the second most common method (27%) among suicides after hanging (40%). DISCUSSION: The similarities observed between both causes of death suggest that at least a proportion of UD deaths may be misclassified suicide cases. However, the discrepancies identified in this analysis seem to indicate that not all UD deaths are misclassified suicides.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".