Temporal Changes in Combat Casualties From Afghanistan by Nationality: 2006–2010
Bibliographic record
Abstract
This study sought to evaluate temporal changes in combat deaths and improvised explosive device (IED)-related fatalities among three coalition allies in Afghanistan: the United States, Canada, and Great Britain. The website icasualties.org was used to identify American, Canadian, and British soldiers killed in combat in Afghanistan between 2006 and 2010. Population-at-risk was determined as the number of personnel serving within the Afghanistan theater for each coalition nation. Unadjusted incidence rates of combat deaths per deployed personnel, and IED deaths as a portion of total combat deaths, were derived and adjusted comparisons performed to control for confounders. Between 2006 and 2010, 1,673 combat deaths occurred in a population of 721,520 soldiers. Fifty percent of all combat deaths occurred as a result of IED attack. British personnel maintained the highest unadjusted risks of combat-related death, as well as IED-associated mortality. As compared to Americans, Canadian personnel were at a significantly increased risk of combat-related death and IED-related fatality. Among Americans, there was a significant reduction in IED-related deaths between 2010 and 2009. For Canadians, no significant change in IED fatalities as compared to total number of troops, or total combat deaths, was appreciated at any point in the study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".