{"id":"W1989071338","doi":"10.1007/s13244-011-0140-1","title":"Pulmonary perfusion imaging using MRI: clinical application","year":2011,"lang":"en","type":"article","venue":"Insights into Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto General Hospital; University Health Network","funders":"Bundesministerium für Bildung und Forschung","keywords":"Medicine; Neuroradiology; Perfusion; Perfusion scanning; Radiology; Interventional radiology; Lung; Magnetic resonance imaging; Internal medicine; Neurology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001761191,0.0001785133,0.0002424219,0.0001429818,0.0002419479,0.00001633072,0.0001507344,0.00005665255,0.00005849425],"category_scores_gemma":[0.00002912203,0.0001559507,0.0001190657,0.0002357021,0.0001863037,0.0002791118,0.0001292566,0.0003012591,0.0000525513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056156,"about_ca_system_score_gemma":0.00006005318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000209715,"about_ca_topic_score_gemma":0.000002341575,"domain_scores_codex":[0.9985993,0.00003034968,0.0004839633,0.0004779423,0.0001752901,0.0002331365],"domain_scores_gemma":[0.9988961,0.00003649469,0.0001626464,0.0005842332,0.0001624749,0.0001581076],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009461614,0.0005985326,0.1014167,0.00004505816,0.00001029888,0.0001661083,0.001200507,0.00001633398,0.1209283,0.007340526,0.0003771588,0.7678059],"study_design_scores_gemma":[0.001369107,0.00009767047,0.0490331,0.0005758529,0.0003435884,0.001489387,0.00118015,0.6364183,0.05571608,0.07648998,0.1761791,0.001107658],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08413967,0.0008977756,0.8835245,0.0006018865,0.0001327625,0.0006725834,8.294403e-7,0.0004878226,0.02954215],"genre_scores_gemma":[0.8054322,0.0001689526,0.1932019,0.0007573545,0.0002549872,0.00005359442,0.00001715662,0.00004031621,0.0000735331],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7666982,"threshold_uncertainty_score":0.6359483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05354302612807407,"score_gpt":0.3758724439465729,"score_spread":0.3223294178184989,"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."}}