{"id":"W4388557294","doi":"10.1093/milmed/usad087","title":"Trauma THOMPSON: Clinical Decision Support for the Frontline Medic","year":2023,"lang":"en","type":"article","venue":"Military Medicine","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"U.S. Army Medical Research Acquisition Activity","keywords":"Medicine; Military medicine; Medical emergency; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003829934,0.0001478261,0.0003045818,0.0001017308,0.0002731155,0.000006033369,0.001416333,0.00009368409,0.0001704016],"category_scores_gemma":[0.0008356842,0.00008488205,0.0001295729,0.0007415685,0.0003348667,0.0001177737,0.0002394776,0.0002239746,0.0002408712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003095942,"about_ca_system_score_gemma":0.00007180265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008009134,"about_ca_topic_score_gemma":0.0001073048,"domain_scores_codex":[0.997808,0.000088078,0.0007078611,0.0005089139,0.0005480862,0.0003390402],"domain_scores_gemma":[0.9947413,0.003646488,0.00006894184,0.001252613,0.0001044672,0.0001862052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002796235,0.00005740605,0.0001120271,0.00001311449,0.00003950544,0.00001081187,0.0006606285,0.0001520247,0.00002287736,0.004052854,0.4845922,0.5102586],"study_design_scores_gemma":[0.001989151,0.0005792624,0.02697362,0.0000571881,0.0000604316,0.00002257827,0.000480959,0.4300166,0.00002009982,0.01239682,0.5272565,0.0001466991],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001831893,0.001333082,0.9002462,0.093199,0.00154831,0.000733537,0.00001928315,0.0003768489,0.0007118507],"genre_scores_gemma":[0.7172205,0.01045375,0.1944366,0.04056905,0.01396914,0.002616042,0.0013416,0.0001825843,0.01921077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7153886,"threshold_uncertainty_score":0.346139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08532549793840508,"score_gpt":0.3998895322304611,"score_spread":0.314564034292056,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}