{"id":"W6977647564","doi":"10.6084/m9.figshare.c.6580915","title":"Artificial intelligence and machine learning for hemorrhagic trauma care","year":2023,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Defence Research and Development Canada","funders":"","keywords":"Trauma care; Coagulopathy; MEDLINE; Predictive modelling; Patient care; Clinical trial; Outcome (game theory); Test (biology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003880543,0.0008166113,0.00218616,0.007710306,0.00051316,0.003884205,0.001980044,0.002499645,0.1693703],"category_scores_gemma":[0.02930059,0.0003664816,0.003301024,0.008575751,0.0007553239,0.004075776,0.001873739,0.002773652,0.02057312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002245932,"about_ca_system_score_gemma":0.004323245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001887364,"about_ca_topic_score_gemma":0.002342946,"domain_scores_codex":[0.9973901,0.00124988,0.000501618,0.000197509,0.000571617,0.00008929723],"domain_scores_gemma":[0.9728717,0.02202171,0.001605788,0.0005592864,0.002560347,0.0003811014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002146795,0.00006298967,0.0005579599,0.1498874,0.0009414344,0.0002241535,0.0001361073,0.001007329,0.0001828415,0.01917068,0.3432629,0.4843516],"study_design_scores_gemma":[0.0001937143,0.0001122081,0.002552648,0.1270007,0.001263394,0.0006321024,0.0001411792,0.0007857081,0.0002318691,0.03809441,0.8289166,0.00007538378],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008054328,0.9192488,0.00467735,0.02533647,0.005698316,0.0005480148,0.01127768,0.0009771126,0.03143078],"genre_scores_gemma":[0.02080448,0.9236627,0.01295255,0.01499999,0.004235984,0.001431977,0.007511268,0.0004348125,0.01396623],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1693703,"threshold_uncertainty_score":0.5666001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1325869551558401,"score_gpt":0.3225173770037684,"score_spread":0.1899304218479283,"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."}}