{"id":"W3109979233","doi":"10.24908/pocus.v5i2.14429","title":"Creating an Efficient Point-of-Care Ultrasound Workflow","year":2020,"lang":"en","type":"article","venue":"POCUS Journal","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Workflow; Point of care ultrasound; Computer science; Ultrasound; Point of care; Point (geometry); Radiology; Medicine; Nursing; Database; Mathematics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002598478,0.0001186121,0.0002978515,0.00004255405,0.00016206,0.00004488598,0.0001721089,0.0000780361,0.0009613768],"category_scores_gemma":[0.001765662,0.00009504914,0.0001707707,0.0002614836,0.00009228048,0.00006483294,0.00002226194,0.0005788705,0.0000829305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005350434,"about_ca_system_score_gemma":0.0001240385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004242809,"about_ca_topic_score_gemma":0.000001089357,"domain_scores_codex":[0.9986131,0.00005473444,0.0005531224,0.000201512,0.0003550153,0.0002224993],"domain_scores_gemma":[0.9981436,0.0005748532,0.0002247299,0.0002271387,0.0002836844,0.000545993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0018561,0.00303195,0.6297457,0.0006384786,0.0007012372,0.0001907556,0.04079258,0.005068608,0.1984531,0.004691416,0.005614983,0.1092151],"study_design_scores_gemma":[0.02267072,0.01691784,0.7627165,0.00274109,0.003007407,0.009327168,0.03762313,0.01045099,0.06293342,0.01172969,0.05735277,0.002529282],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9783015,0.0005465129,0.008227816,0.003023855,0.0001286165,0.000227403,0.00001452707,0.00005819995,0.009471568],"genre_scores_gemma":[0.9640143,0.00005235534,0.03400061,0.0009904422,0.0008603206,0.000004797383,0.00001390946,0.00002409714,0.00003918046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1355197,"threshold_uncertainty_score":0.9999519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03938454411201391,"score_gpt":0.3422316484410558,"score_spread":0.3028471043290419,"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."}}