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Record W2187313585 · doi:10.7205/milmed-d-12-00403

Temporal Changes in Combat Casualties From Afghanistan by Nationality: 2006–2010

2013· article· en· W2187313585 on OpenAlexaboutno aff
Andrew J. Schoenfeld, James H. Nelson, Robert T. Burks, Philip J. Belmont

Bibliographic record

VenueMilitary Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyMedicinePopulationMilitary personnelNavyCase fatality rateEnvironmental healthCivilian populationGeographyPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.060
GPT teacher head0.380
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2013
Admission routes1
Has abstractyes

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