Bibliographic record
Abstract
There may be misconceptions around the real causes of death in the Canadian Forces (CF) as public attention focuses on combat deaths. Objective. To compare the proportion of deaths from natural causes to traumatic causes in the CF. Methods. Retrospective chart review of all CF deaths (1983--2003), classifying deaths as either natural (neoplasm, cardiovascular and other) or traumatic (inadvertent, suicide, homicide or combat). Preventable deaths were assessed using explicit criteria. Results. A total of 1617 CF members died. Traumatic deaths constituted 56% (n=813) and natural deaths were 44% (n=651). Motor vehicle crashes caused 24% (n=351), neoplasms 22% (n=319), cardiovascular diseases 18% (n=259) and suicide 17% (n=248). Combat deaths were only 0.4% (n=6). Discussion. Combat deaths were rare. More military lives might be saved by focusing on prevention strategies for common causes of death including cancer, cardiovascular disease and motor vehicle collisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".