{"id":"W2804472574","doi":"10.2196/10727","title":"Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Disaster Response and Management","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institutes of Health","keywords":"Triage; Mass-casualty incident; Emergency department; Medical emergency; Interactive kiosk; Medicine; Incident report; Decision support system; Emergency medicine; Poison control; Human factors and ergonomics; Computer science; Nursing; Computer security; Artificial intelligence","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":[0.003692483,0.0004903166,0.0004477375,0.000759798,0.0004317461,0.001253173,0.0004241612,0.0009131695,0.004594394],"category_scores_gemma":[0.01937522,0.0002521136,0.0007716318,0.0003652705,0.0003548531,0.001550643,0.0008917171,0.0007233918,0.0008821065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000531977,"about_ca_system_score_gemma":0.000874074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009675656,"about_ca_topic_score_gemma":0.001460429,"domain_scores_codex":[0.9985937,0.0006378428,0.0001454018,0.000103409,0.0003470892,0.0001725565],"domain_scores_gemma":[0.9904292,0.006439386,0.0005371372,0.0002665847,0.00171744,0.0006103776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"nonrandomized_trial","study_design_scores_codex":[0.01341686,0.07350173,0.338762,0.008024891,0.0003888775,0.002428539,0.02817217,0.001237348,0.007258169,0.00103236,0.007640966,0.5181361],"study_design_scores_gemma":[0.004729365,0.1991556,0.6887795,0.003360321,0.001444389,0.004604487,0.03952298,0.01061288,0.008187983,0.0007154229,0.03861862,0.0002684733],"study_design_candidate":"nonrandomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938685,0.0005125115,0.0006952918,0.0001725233,0.0000388714,0.002663654,0.0002660051,0.00002684879,0.00175574],"genre_scores_gemma":[0.9847451,0.001504616,0.00576113,0.0005087423,0.00009342629,0.00451957,0.0004527149,0.00002640022,0.00238837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004594394,"threshold_uncertainty_score":0.01952791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1122731278274016,"score_gpt":0.528302453792691,"score_spread":0.4160293259652894,"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."}}