{"id":"W2096926032","doi":"10.1017/s1049023x1400140x","title":"Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage","year":2015,"lang":"en","type":"article","venue":"Prehospital and Disaster Medicine","topic":"Disaster Response and Management","field":"Health Professions","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Triage; Telemedicine; Medical emergency; Intervention (counseling); Medicine; Mass-casualty incident; Emergency management; Emergency medicine; Nursing; Poison control; Health care; Human factors and ergonomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006863746,0.0004441585,0.0007886917,0.0001850642,0.0003312464,0.00006761576,0.000274593,0.000190401,0.000462347],"category_scores_gemma":[0.0003284078,0.0002927689,0.00008583258,0.0002078853,0.0002745963,0.0009403198,0.0004456537,0.0003196556,0.000142686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112038,"about_ca_system_score_gemma":0.00008909756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003544385,"about_ca_topic_score_gemma":0.0001224112,"domain_scores_codex":[0.9968829,0.0003146253,0.0008295678,0.0007134996,0.0005715682,0.0006878545],"domain_scores_gemma":[0.9976932,0.000534443,0.0003007824,0.0006170322,0.0001802639,0.0006742313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.006840977,0.0007522493,0.02121722,0.001502962,0.0007045673,0.0001349829,0.7595655,0.00001652598,0.001823716,0.004822462,0.1462768,0.05634199],"study_design_scores_gemma":[0.02363234,0.002179319,0.003997943,0.003229946,0.0006038457,0.000006268257,0.5288204,0.001714092,0.0000229854,0.01372342,0.4213354,0.000734005],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605718,0.006075315,0.006977378,0.004813961,0.003733299,0.00254011,0.00005529943,0.0001109419,0.0151219],"genre_scores_gemma":[0.9768275,0.0002016284,0.0006136862,0.004906361,0.0030279,0.0003582428,0.0002342052,0.0000840681,0.01374643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2750586,"threshold_uncertainty_score":0.9999524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1138973791501267,"score_gpt":0.3900544851801158,"score_spread":0.2761571060299891,"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."}}