{"id":"W2010066628","doi":"10.1007/s11548-011-0621-1","title":"Accuracy considerations in image-guided cardiac interventions: experience and lessons learned","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research","keywords":"Context (archaeology); Medical physics; Computer science; Image registration; Medical imaging; Intracardiac injection; Medicine; Artificial intelligence; Computer vision; Image (mathematics); Surgery","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002610672,0.0000821464,0.0002276811,0.0002563711,0.00003762779,0.00004185399,0.00008565128,0.00005696057,0.00002996988],"category_scores_gemma":[0.000108459,0.00007847754,0.0001032432,0.00005802355,0.0001033053,0.0001980486,0.00003041239,0.000161446,0.000001249596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002307998,"about_ca_system_score_gemma":0.00002748682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000767655,"about_ca_topic_score_gemma":0.000003035607,"domain_scores_codex":[0.9992103,0.00006461058,0.0004691531,0.00009216338,0.00007036843,0.00009340956],"domain_scores_gemma":[0.9989995,0.0006239258,0.0001156381,0.00006713177,0.0001393784,0.00005439601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001499504,0.0008292017,0.5547848,0.0001902969,0.00312602,0.001341509,0.01486355,0.008928508,0.01070962,0.04567813,0.04140829,0.3179901],"study_design_scores_gemma":[0.0005983212,0.00003582549,0.9754941,0.0002725338,0.0000422579,0.002433124,0.0002018443,0.01082362,0.0005196659,0.007770504,0.001541162,0.0002669702],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9055637,0.002356116,0.08830129,0.00165321,0.001740069,0.00007425612,0.000009472205,0.00003692811,0.0002650169],"genre_scores_gemma":[0.9891576,0.001389118,0.009213923,0.00009131679,0.0001251803,0.000007714018,0.000003999495,0.000006929602,0.000004262258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4207093,"threshold_uncertainty_score":0.3200221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1119962559554026,"score_gpt":0.3403788626406026,"score_spread":0.2283826066851999,"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."}}