{"id":"W1620178361","doi":"10.1002/jmri.24285","title":"Whole body MRI: Improved lesion detection and characterization with diffusion weighted techniques","year":2013,"lang":"en","type":"review","venue":"Journal of Magnetic Resonance Imaging","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Diffusion MRI; Whole body imaging; Radiology; Functional imaging; Magnetic resonance imaging","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003423152,0.000419207,0.001426925,0.0005102628,0.00009127399,0.0001176913,0.0001556298,0.0001860427,0.00007865111],"category_scores_gemma":[0.00004143012,0.0002774512,0.0001984772,0.0003465369,0.0001059232,0.0003111147,0.0000691751,0.0007343816,0.000005540941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002730452,"about_ca_system_score_gemma":0.0002358261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001962742,"about_ca_topic_score_gemma":0.000002041362,"domain_scores_codex":[0.997785,0.0001280546,0.0009911261,0.0003571099,0.0004627023,0.0002759929],"domain_scores_gemma":[0.9976205,0.0001051575,0.001395305,0.0003186292,0.000386491,0.0001739835],"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":[0.00007723836,0.00006187139,0.0002765414,0.002993917,0.00001542213,0.00006132253,0.00002464297,3.635771e-9,0.009646563,5.887071e-7,0.000268521,0.9865733],"study_design_scores_gemma":[0.0008233524,0.0008697482,0.002237712,0.04093629,0.0009192873,0.002173388,0.00001184209,0.0002816544,0.0007078812,0.000007382023,0.9507608,0.0002707028],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009115433,0.9954407,0.001376695,0.0006584874,0.0002090982,0.001258035,0.000007294555,0.0000432902,0.00009481461],"genre_scores_gemma":[0.0001261756,0.9938458,0.004724444,0.00009761396,0.0006082934,0.000106979,0.00001845,0.00009335167,0.0003788932],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9863027,"threshold_uncertainty_score":0.9999678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463145724012631,"score_gpt":0.2894303034580375,"score_spread":0.2747988462179112,"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."}}