{"id":"W2013681472","doi":"10.1109/tmi.2014.2321777","title":"Electromagnetic Tracking in Medicine—A Review of Technology, Validation, and Applications","year":2014,"lang":"en","type":"review","venue":"IEEE Transactions on Medical Imaging","topic":"Surgical Simulation and Training","field":"Medicine","cited_by":489,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"Hungarian Scientific Research Fund; Deutsche Forschungsgemeinschaft; Deutsches Krebsforschungszentrum; Nemzeti Kutatási és Technológiai Hivatal; Varian Medical Systems","keywords":"Computer science; Robustness (evolution); Tracking (education); Context (archaeology); Tracking system; Medical imaging; Video tracking; Tracking error; Data science; Artificial intelligence; Computer vision; Object (grammar); Kalman filter","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":[0.002830363,0.00103678,0.001848022,0.004105694,0.0004330871,0.001902702,0.001481063,0.001846633,0.003322125],"category_scores_gemma":[0.004708942,0.0005216683,0.0009232831,0.003751044,0.0009818887,0.002218617,0.001010084,0.001610363,0.002202842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008666434,"about_ca_system_score_gemma":0.002347922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001719769,"about_ca_topic_score_gemma":0.001473273,"domain_scores_codex":[0.998836,0.0002406057,0.0002106183,0.0001620223,0.0004984061,0.00005236603],"domain_scores_gemma":[0.995827,0.002775331,0.0003195334,0.0001091495,0.0008714958,0.00009747448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005413228,0.00006829917,0.0003642426,0.02854558,0.00009738544,0.0001492158,0.00009233371,0.000646065,0.001982132,0.003895466,0.0128508,0.9512545],"study_design_scores_gemma":[0.00001813475,0.0002788081,0.001713737,0.01543386,0.0003568629,0.002415845,0.0001588535,0.0006500326,0.002943368,0.003943884,0.9719983,0.0000883247],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001625504,0.9974107,0.0008539114,0.0002931198,0.0002247185,0.00001024963,0.00002164133,0.00001346685,0.001009606],"genre_scores_gemma":[0.00121048,0.9969937,0.0009706644,0.0001873612,0.0002768161,0.00001491607,0.00003331024,0.00000471605,0.0003080477],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004105694,"threshold_uncertainty_score":0.01496851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03295844657262619,"score_gpt":0.3837409358765452,"score_spread":0.350782489303919,"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."}}