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Record W2071347855 · doi:10.7205/milmed-d-11-00044

Physical Surrogate Leg to Evaluate Blast Mine Injury

2011· article· en· W2071347855 on OpenAlexafffund
Duane S. Cronin, Kevin Williams, Chris Salisbury

Bibliographic record

VenueMilitary Medicine · 2011
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsDefence Research and Development CanadaUniversity of Waterloo
FundersMinistère de la Défense NationaleOntario Centres of Excellence
KeywordsMedicineBlast injuryPercentileAmputationPoison controlInjury preventionPhysical therapyPhysical medicine and rehabilitationSurgeryEmergency medicineStatistics

Abstract

fetched live from OpenAlex

Antipersonnel blast landmines pose a significant threat in affected areas, with injuries to the lower extremity and amputation being common. Addressing a need for injury prediction and protection evaluation, a 50th percentile physical surrogate lower leg was developed incorporating the load transmission paths in the lower leg. Biofidelic and frangible materials were evaluated and selected based on high deformation rate properties compared to those for human tissues. The predicted leg injuries from experimental blast testing were in agreement with injury data for unprotected and protected legs. Post-test examination was found to be the only consistent and reliable evaluation method for predicting injury outcome, and an evaluation based on tissue damage was shown to be sensitive to changes in loading conditions, not possible with existing approaches. This study identified the severity of calcaneal fracture as the primary determinant of serious injury, which should be the focus of future protection development.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.996

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)0.0040.005

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 head0.124
GPT teacher head0.442
Teacher spread0.318 · 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; both teacher heads agree on what is shown here.

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

Citations11
Published2011
Admission routes2
Has abstractyes

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