{"id":"W2992648114","doi":"10.1503/cjs.002619","title":"A “human-proof pointy-end”: a robotically applied hemostatic clamp for care-under-fire","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Surgery","topic":"Trauma, Hemostasis, Coagulopathy, Resuscitation","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Bleed; Damage control surgery; Mass-casualty incident; Robot; Law enforcement; Medical emergency; Mass Casualty; Damage control; Control (management); Surgery; Poison control; Injury prevention; Resuscitation; Law; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008780483,0.0005762288,0.0002735474,0.0003806382,0.0007662143,0.0008251722,0.001160872,0.001133195,0.007709617],"category_scores_gemma":[0.001516351,0.0001672918,0.0006898058,0.0001163584,0.000901863,0.0009308725,0.001112933,0.001440492,0.001709847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003714754,"about_ca_system_score_gemma":0.0005884461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007245478,"about_ca_topic_score_gemma":0.001038817,"domain_scores_codex":[0.9995905,0.00006575171,0.00004326396,0.00007944697,0.0001590872,0.00006206242],"domain_scores_gemma":[0.9996232,0.00008680387,0.00004629495,0.00007336142,0.0000483482,0.000122043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003981521,0.001614315,0.01309815,0.001802117,0.0004879227,0.03932794,0.00152322,0.009076987,0.2673953,0.01843472,0.1009198,0.5423381],"study_design_scores_gemma":[0.000746319,0.01617302,0.03004204,0.0008600793,0.0005682582,0.1631225,0.001018013,0.05303752,0.2561932,0.008174357,0.4694704,0.0005944037],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3996925,0.004231896,0.5011983,0.01696303,0.01002155,0.001032277,0.0007332136,0.006812844,0.05931441],"genre_scores_gemma":[0.7660105,0.001942899,0.1961604,0.005597511,0.0007134288,0.0003326347,0.0006088625,0.000288289,0.02834548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007709617,"threshold_uncertainty_score":0.02579123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03827209365264579,"score_gpt":0.2761194440105293,"score_spread":0.2378473503578836,"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."}}