{"id":"W2564419124","doi":"10.1530/erp-16-0036","title":"Making three-dimensional echocardiography more tangible: a workflow for three-dimensional printing with echocardiographic data","year":2016,"lang":"en","type":"review","venue":"Echo Research and Practice","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; University Health Network","funders":"","keywords":"Workflow; 3D printing; Rapid prototyping; Three dimensional printing; Process (computing); Medicine; Segmentation; Computer science; 3D modeling; Engineering drawing; Artificial intelligence; Database; Engineering; Mechanical engineering","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":[0.00412914,0.001337463,0.001471836,0.005647283,0.0005779322,0.004035298,0.002016807,0.001632159,0.005043997],"category_scores_gemma":[0.003926422,0.0007111148,0.001376921,0.003165139,0.001743297,0.003439576,0.002395752,0.002593102,0.006387158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006754604,"about_ca_system_score_gemma":0.002364671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00118703,"about_ca_topic_score_gemma":0.001520436,"domain_scores_codex":[0.998,0.0004155407,0.0003651611,0.0002053589,0.000947036,0.0000669513],"domain_scores_gemma":[0.9978719,0.001069546,0.0001786099,0.0001989098,0.0005727975,0.000108345],"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":[0.00003834381,0.00006110471,0.0003724025,0.01018659,0.00006778707,0.0005371213,0.0005311446,0.0007955898,0.006025121,0.01411014,0.02038053,0.946894],"study_design_scores_gemma":[0.00001205474,0.0000563431,0.0009261809,0.004704046,0.00007789071,0.003267873,0.0002996599,0.0007271314,0.005329663,0.009754036,0.9747555,0.00008962131],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001485954,0.8565147,0.1150533,0.004031408,0.001772295,0.0003425672,0.0005507434,0.001235579,0.01901357],"genre_scores_gemma":[0.006350261,0.8299583,0.1560859,0.001100228,0.000677851,0.0002364069,0.0006246246,0.000223547,0.004742902],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005647283,"threshold_uncertainty_score":0.02183723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2399782083265551,"score_gpt":0.5137346756768665,"score_spread":0.2737564673503113,"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."}}