Sudden Natural Deaths in Ontario, Canada: A Retrospective Autopsy Analysis (2012–2016)
Bibliographic record
Abstract
This study was performed to identify the categories and distribution of sudden natural deaths (SNDs) in Ontario (ON) from January 2012 to December 2016 as no such reports have been published in ON, and the authors sought to find out the distribution of SND across ON by organ system, age, and sex. Three medicolegal databases were searched, and eight major categories of SND were identified and evaluated using multinomial logistic regression. During the 5-year period, 10,880 autopsies were performed on individuals aged 1–100, who died of sudden and natural causes. Over 800 causes of SNDs were recorded from January 2012 to December 2016. The largest category of SND was attributed to diseases and complications of the cardiovascular system (64.1%) followed by the respiratory system (9.1%), gastrointestinal system (6.9%), central nervous system (6.0%), metabolic diseases (3.8%), chronic alcoholism (3.5%), other (2.4%), infectious diseases (2.2%), and cancer (1.8%). The five most common causes of SND were also cardiovascular in origin, which included atherosclerotic heart disease (n = 2127, 19.5%), atherosclerotic and hypertensive heart disease (n = 711, 6.5%), myocardial infarction (n = 723, 6.6%), hypertensive heart disease (n = 518, 4.8%), and pulmonary embolism (n = 377, 3.5%). Determination of cause of death in natural deaths is an important part in death investigation, which can provide crucial information in the interest of public health by identifying public health risks and monitoring disease trends.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".