{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002086275,0.0002834224,0.0004637736,0.0002199966,0.001634114,0.00002190579,0.000174543,0.0001633362,0.0004144955],"category_scores_gemma":[0.0001197073,0.0002461071,0.00004458903,0.0002017482,0.00006909948,0.0002394592,0.0002632466,0.0002872587,0.000157279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157278,"about_ca_system_score_gemma":0.0004883706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000184177,"about_ca_topic_score_gemma":0.0007220803,"domain_scores_codex":[0.996266,0.0004781829,0.001108158,0.0006592888,0.0004279776,0.001060376],"domain_scores_gemma":[0.9981039,0.0002359954,0.0003765295,0.0004357279,0.0002036202,0.0006442492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00870591,0.00200034,0.4759168,0.004774131,0.0001360876,0.00004802255,0.08353641,3.804782e-7,0.00002019311,0.0006133046,0.2599843,0.1642641],"study_design_scores_gemma":[0.0139031,0.0184499,0.4983944,0.0001809428,0.0002796297,0.000009599353,0.03139977,0.0002415869,0.000005822073,0.00355455,0.4328413,0.00073939],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864881,0.0001893455,0.001403203,0.0001551599,0.002260341,0.007790291,0.0001264312,0.00008889398,0.001498213],"genre_scores_gemma":[0.9897513,0.0006016564,0.0007832135,0.002341802,0.0009964079,0.002966993,0.00006279758,0.00004787669,0.002447994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1728571,"threshold_uncertainty_score":0.9999991,"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."}}