{"id":"W2045999511","doi":"10.1016/j.media.2007.10.006","title":"Performance evaluation of a medical robotic 3D-ultrasound imaging system","year":2007,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Montreal Clinical Research Institute; Université de Montréal","funders":"","keywords":"Imaging phantom; Scanner; Computer science; Repeatability; Computer vision; Artificial intelligence; Stenosis; Ground truth; Robot; Biomedical engineering; Medicine; Nuclear medicine; Radiology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007793732,0.0001830734,0.0006264202,0.0005195501,0.00007058702,0.00002343051,0.0002883729,0.0001030934,0.006899635],"category_scores_gemma":[0.001302316,0.0001457794,0.0003324458,0.001480769,0.0002494864,0.0001463326,0.0001024326,0.000240685,0.00009136215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003005991,"about_ca_system_score_gemma":0.0002775081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001657011,"about_ca_topic_score_gemma":0.0000765993,"domain_scores_codex":[0.9917742,0.0001643832,0.0007935512,0.0003796635,0.006489838,0.0003983903],"domain_scores_gemma":[0.9980224,0.0001503405,0.0001669336,0.0005152363,0.0004401605,0.0007048809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000243317,0.001285488,0.5317069,0.001409554,0.006249665,0.001251165,0.0007775759,0.000716487,0.001354648,0.00007969392,0.002430205,0.4524954],"study_design_scores_gemma":[0.001332441,0.00003574185,0.2967959,0.0002728938,0.008669937,0.00006367474,0.0005784628,0.6917943,0.0002011196,0.000001872662,0.0001278336,0.0001257574],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9194101,0.0004587789,0.06767285,0.001482903,0.0001192268,0.0003371668,0.000002071889,0.0001013518,0.01041557],"genre_scores_gemma":[0.9979289,0.00004065911,0.0009289086,0.000647338,0.0002155717,0.00001973366,0.00008176334,0.00001647323,0.0001206088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6910778,"threshold_uncertainty_score":0.9940082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01504041100557042,"score_gpt":0.3230855610905272,"score_spread":0.3080451500849568,"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."}}