{"id":"W2124465355","doi":"10.1109/iembs.1989.95979","title":"Medical image delays with ACR/NEMA standard","year":2003,"lang":"en","type":"article","venue":"","topic":"Digital Radiography and Breast Imaging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Workload; DICOM; Computer science; Digital image; Center (category theory); Computer vision; Picture archiving and communication system; Medical imaging; Image (mathematics); Medical physics; Artificial intelligence; Multimedia; Image processing; Medicine; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004427008,0.0004384414,0.000393443,0.000986714,0.0004365897,0.001047349,0.0008473764,0.0005461737,0.002163928],"category_scores_gemma":[0.031517,0.0002451242,0.0002668168,0.001133537,0.0004610825,0.001714959,0.0005697419,0.0004600396,0.0004989611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001794314,"about_ca_system_score_gemma":0.0008365043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002332176,"about_ca_topic_score_gemma":0.001524937,"domain_scores_codex":[0.9958197,0.001011512,0.0004171908,0.000382499,0.002045735,0.000323436],"domain_scores_gemma":[0.9761428,0.01091664,0.001534845,0.002465963,0.008530553,0.0004092165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.02671237,0.002534775,0.1049307,0.0009765711,0.0002657919,0.001493716,0.001483044,0.2933201,0.2768241,0.01083987,0.008094473,0.2725245],"study_design_scores_gemma":[0.0005034902,0.01109706,0.05469464,0.0001085454,0.0002630606,0.002495796,0.0009941939,0.5091009,0.4020733,0.004588189,0.01378492,0.0002959363],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690764,0.0004953346,0.02459318,0.0001846678,0.0001126846,0.0002000867,0.0003507307,0.0007314929,0.00425548],"genre_scores_gemma":[0.9846621,0.0001412496,0.01371717,0.00004368177,0.00001890338,0.00005247755,0.0003850022,0.00008447517,0.0008949965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004427008,"threshold_uncertainty_score":0.02341253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004855097584048458,"score_gpt":0.2446643605697292,"score_spread":0.2398092629856808,"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."}}