{"id":"W3158912897","doi":"10.1148/radiol.2021201740","title":"Oncologically Relevant Findings Reporting and Data System (ONCO-RADS): Guidelines for the Acquisition, Interpretation, and Reporting of Whole-Body MRI for Cancer Screening","year":2021,"lang":"en","type":"review","venue":"Radiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Medicine; Cancer; Medical physics; Geneticist; Standardization; Radiology; Internal medicine; Computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01937769,0.001075141,0.00263918,0.0107442,0.0005837186,0.002875694,0.003501581,0.002637298,0.008236923],"category_scores_gemma":[0.03269056,0.0009805131,0.002642541,0.006449768,0.001794578,0.002209841,0.002537318,0.00403,0.01276891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002069983,"about_ca_system_score_gemma":0.01233741,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005135417,"about_ca_topic_score_gemma":0.004664137,"domain_scores_codex":[0.9861012,0.005184399,0.003967097,0.000429636,0.003916513,0.000401298],"domain_scores_gemma":[0.96203,0.01544119,0.005466957,0.002082803,0.01415313,0.0008259134],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003408613,0.0001017407,0.001154999,0.02084418,0.000305613,0.0003267679,0.0002759338,0.0005573397,0.00186435,0.01151115,0.3342289,0.6284881],"study_design_scores_gemma":[0.00007503516,0.00005228297,0.002792193,0.01022654,0.0001688615,0.0008516642,0.0000853954,0.0001387234,0.0009517488,0.002404298,0.9822056,0.00004771727],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.002682182,0.8102837,0.05319623,0.03034473,0.007053446,0.00655978,0.02481706,0.004725727,0.06033731],"genre_scores_gemma":[0.01459539,0.7120762,0.1637861,0.01490202,0.003305911,0.008531958,0.05923958,0.001597995,0.02196491],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9806223,"threshold_uncertainty_score":0.1024803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1796389452340031,"score_gpt":0.4886687240399449,"score_spread":0.3090297788059418,"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."}}