{"id":"W4387563800","doi":"10.1002/jmri.29049","title":"Best Practice for <scp>MRI</scp> Diagnostic Accuracy Research With Lessons and Examples from the <scp>LI‐RADS</scp> Individual Participant Data Group","year":2023,"lang":"en","type":"review","venue":"Journal of Magnetic Resonance Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Queen Elizabeth II Health Sciences Centre; Juravinski Hospital; McMaster University; University of Toronto; Dalhousie University; Hamilton Health Sciences","funders":"Radiological Society of North America","keywords":"Documentation; Computer science; Data collection; Medical physics; Raw data; Data science; Best practice; Diagnostic accuracy; Data mining; Medicine; Radiology; Statistics","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":[0.1757008,0.001833498,0.004435519,0.01302176,0.002091407,0.01038866,0.006621337,0.009065055,0.01144428],"category_scores_gemma":[0.3593559,0.0019483,0.005836601,0.01087404,0.006711532,0.01426153,0.00576064,0.0122866,0.009275612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007616196,"about_ca_system_score_gemma":0.03636564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009930843,"about_ca_topic_score_gemma":0.01638082,"domain_scores_codex":[0.8584512,0.07062906,0.03425627,0.004899342,0.03054601,0.001218254],"domain_scores_gemma":[0.5088969,0.3372268,0.02737686,0.02534163,0.09736243,0.003795297],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001584087,0.00009149041,0.00078804,0.13149,0.0007318536,0.0003455543,0.002124454,0.0004774891,0.0006378992,0.02436307,0.2716279,0.5671638],"study_design_scores_gemma":[0.0001231014,0.0001093934,0.001068186,0.3251467,0.0005826029,0.000602433,0.00084018,0.0002709298,0.0003961969,0.03178574,0.6389474,0.0001271065],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"methods","genre_scores_codex":[0.0003238891,0.7448733,0.02663981,0.2010501,0.01343538,0.001958273,0.0007039225,0.0005558571,0.01045933],"genre_scores_gemma":[0.006421427,0.7592676,0.1581587,0.06005114,0.006271192,0.005131223,0.0009030048,0.0004102526,0.003385488],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8242992,"threshold_uncertainty_score":0.9292058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2645229670947704,"score_gpt":0.4632675049421243,"score_spread":0.1987445378473539,"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."}}