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Record W2797882406 · doi:10.1136/jramc-2018-000956

Guidelines for using animal models in blast injury research

2018· article· en· W2797882406 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.

Bibliographic record

VenueJournal of the Royal Army Medical Corps · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsBlast injuryAnimal modelTask (project management)Set (abstract data type)MedicineComputer sciencePoison controlMedical emergencyEngineeringSystems engineering

Abstract

fetched live from OpenAlex

Blast injury is a very complex phenomenon and frequently results in multiple injuries. One method to investigate the consequences of blast injuries is with the use of living systems (animal models). The use of animals allows the examination and evaluation of injury mechanisms in a more controlled manner, allowing variables such as primary or secondary blast injury for example, to be isolated and manipulated as required. To ensure a degree of standardisation across the blast research community a set of guidelines which helps researchers navigate challenges of modelling blast injuries in animals is required. This paper describes the guidelines for Using Animal Models in Blast Injury Research developed by the NATO Health Factors and Medicine (HFM) Research Task Group 234.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.405
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
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 science0.0010.001
Research integrity0.0000.001
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 head0.473
GPT teacher head0.581
Teacher spread0.108 · 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