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Record W4417000028 · doi:10.1080/24725838.2025.2594433

Battlefield Triage Technology: Addressing Effectiveness in Far-Forward Combat Casualty Care

2025· article· en· W4417000028 on OpenAlexaff
Katie Shea, Elizabeth Wong, Craig N. Burrell

Bibliographic record

VenueIISE Transactions on Occupational Ergonomics and Human Factors · 2025
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of WaterlooDepartment of National Defence
Fundersnot available
KeywordsTriageBattlefieldIdentification (biology)Health careTelemedicineHealth technologyMedical care

Abstract

fetched live from OpenAlex

OCCUPATIONAL APPLICATIONSWe identified strategies to support the development or evaluation of technology solutions for a medical response in remote and austere environments. We conducted a systematic analysis of relevant tasks and potential errors within battlefield medical response and communicated the resultant system taxonomy for interdisciplinary application by both engineers and health care practitioners. Our paper contributes to continued advancements of technology support for remote casualty care through detailed insights into remote medical response, identification of system vulnerabilities, and a consolidated system analysis.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.415
Teacher spread0.354 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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