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Record W2119716066 · doi:10.1503/cjs.025011

Tactical Combat Casualty Care in the Canadian Forces: lessons learned from the Afghan war

2011· review· en· W2119716066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Surgery · 2011
Typereview
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsCanadian Forces CollegeCanadian Armed Forces
Fundersnot available
KeywordsBattlefieldMedicineAfghanMilitary medicinePsychological interventionTrauma careMedical emergencyMilitary personnelNursingLawPolitical science

Abstract

fetched live from OpenAlex

Tactical Combat Casualty Care (TCCC) is intended to treat potentially preventable causes of death on the battlefield, but acknowledges that application of these treatments may place the provider and even the mission in jeopardy if performed at the wrong time. Therefore, TCCC classifies the tactical situation with respect to health care provision into 3 phases (care under fire, tactical field care and tactical evacuation) and only permits certain interventions to be performed in specific phases based on the danger to the provider and casualty. In the 6 years that the Canadian Forces (CF) have been involved in sustained combat operations in Kandahar, Afghanistan, more than 1000 CF members have been injured and more than 150 have been killed. As a result, the CF gained substantial experience delivering TCCC to wounded soldiers on the battlefield. The purpose of this paper is to review the principles of TCCC and some of the lessons learned about battlefield trauma care during this conflict.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.321
GPT teacher head0.379
Teacher spread0.058 · 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